diff --git a/conda-env/ci.yml b/conda-env/ci.yml
index 4e9b162d9..46df89981 100644
--- a/conda-env/ci.yml
+++ b/conda-env/ci.yml
@@ -13,7 +13,9 @@ dependencies:
     - netcdf4
     - numpy >=2.0.0,<3.0.0
     - pandas
+    - pooch >=1.8
     - python-dateutil
+    - regionmask
     - scipy
     - sparse
     - xarray >=2024.03.0
@@ -26,4 +28,3 @@ dependencies:
     # ==================
     - pytest
     - pytest-cov
-    - pooch # Required for xarray tutorial data
diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index 82f688b98..9cabb0d11 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -13,7 +13,9 @@ dependencies:
     - netcdf4
     - numpy >=2.0.0,<3.0.0
     - pandas
+    - pooch >=1.8
     - python-dateutil
+    - regionmask
     - scipy
     - sparse
     - xarray >=2024.03.0
@@ -34,7 +36,6 @@ dependencies:
     - pandoc
     - ipython # Required for nbsphinx syntax highlighting
     - gsw-xarray # Required for vertical regridding example
-    - pooch # Required for xarray tutorial data
     # Quality Assurance
     # ==================
     - types-python-dateutil
diff --git a/docs/_static/thumbnails/spatial-landsea-mask.png b/docs/_static/thumbnails/spatial-landsea-mask.png
new file mode 100644
index 000000000..a9cbf8517
Binary files /dev/null and b/docs/_static/thumbnails/spatial-landsea-mask.png differ
diff --git a/docs/api.rst b/docs/api.rst
index e625146d3..987762b34 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -39,6 +39,17 @@ Below is a list of top-level API functions that are available in ``xcdat``.
     create_zonal_grid
     tutorial.open_dataset
 
+Module-level API Functions
+--------------------------
+
+Below is a list of module-level API functions that are available in ``xcdat``.
+
+.. autosummary::
+    :toctree: generated/
+
+    mask.pcmdi_land_sea_mask
+
+
 Accessors
 ---------
 
@@ -123,6 +134,9 @@ Methods
     Dataset.bounds.get_bounds
     Dataset.bounds.add_missing_bounds
     Dataset.spatial.average
+    Dataset.spatial.mask_land
+    Dataset.spatial.mask_sea
+    Dataset.spatial.generate_land_sea_mask
     Dataset.temporal.average
     Dataset.temporal.group_average
     Dataset.temporal.climatology
diff --git a/docs/examples/spatial-landsea-mask.ipynb b/docs/examples/spatial-landsea-mask.ipynb
new file mode 100644
index 000000000..7407eb38b
--- /dev/null
+++ b/docs/examples/spatial-landsea-mask.ipynb
@@ -0,0 +1,1037 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3d6ebf97-8656-4c44-bf9e-4652ba1d153b",
+   "metadata": {},
+   "source": [
+    "# Spatial Land/Sea Mask\n",
+    "\n",
+    "Authors: [Jason Boutte](https://github.com/jasonb5/) & [Jiwoo Lee](https://github.com/lee1043/) & [Tom Vo](https://github.com/tomvothecoder/)\n",
+    "\n",
+    "Updated: 10/09/25 [xcdat v0.9.1]\n",
+    "\n",
+    "Related APIs:\n",
+    "\n",
+    "- [xarray.Dataset.spatial.mask_land()](../generated/xarray.Dataset.spatial.mask_land.rst)\n",
+    "- [xarray.Dataset.spatial.mask_sea()](../generated/xarray.Dataset.spatial.mask_sea.rst)\n",
+    "- [xarray.Dataset.spatial.generate_land_sea_mask()](../generated/xarray.Dataset.spatial.generate_land_sea_mask.rst)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3b6a296-35d4-47d7-ab50-de3ab34707eb",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In geophysical sciences it can often be useful to mask data that is not of interest. Spatial land/sea masking functionality in xcdat allows users to quickly generate and apply land/sea masks derived from various methods.\n",
+    "\n",
+    "In the example below, we demonstrate masking land/sea, generating land/sea masks and customizing the mask generation.\n",
+    "\n",
+    "The data used in this example can be found in the [xcdat-data repository](https://github.com/xCDAT/xcdat-data).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "481d258e-1618-4936-a7f5-5daed7404274",
+   "metadata": {},
+   "source": [
+    "### Notebook Kernel Setup\n",
+    "\n",
+    "Users can [install their own instance of xcdat](../getting-started-guide/installation.rst) and follow these examples using their own environment (e.g., with VS Code, Jupyter, Spyder, iPython) or [enable xcdat with existing JupyterHub instances](../getting-started-guide/getting-started-hpc-jupyter.rst).\n",
+    "\n",
+    "First, create the conda environment:\n",
+    "\n",
+    "```bash\n",
+    "conda create -n xcdat_notebook -c conda-forge xcdat xesmf matplotlib ipython ipykernel cartopy nc-time-axis gsw-xarray jupyter pooch\n",
+    "```\n",
+    "\n",
+    "Then install the kernel from the `xcdat_notebook` environment using `ipykernel` and name the kernel with the display name (e.g., `xcdat_notebook`):\n",
+    "\n",
+    "```bash\n",
+    "python -m ipykernel install --user --name xcdat_notebook --display-name xcdat_notebook\n",
+    "```\n",
+    "\n",
+    "Then to select the kernel `xcdat_notebook` in Jupyter to use this kernel.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59cddcb2-861c-4b16-b977-16cccce82112",
+   "metadata": {},
+   "source": [
+    "## 1. Open the `Dataset`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1a3c9e7-4670-4d80-9882-afcfd557043f",
+   "metadata": {},
+   "outputs": [
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+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 7MB\n",
+       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\n",
+       "Coordinates:\n",
+       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
+       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
+       "    height     float64 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) object 960B ...\n",
+       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
+       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
+       "    tas        (time, lat, lon) float32 7MB ...\n",
+       "Attributes: (12/48)\n",
+       "    Conventions:                     CF-1.7 CMIP-6.2\n",
+       "    activity_id:                     CMIP\n",
+       "    branch_method:                   standard\n",
+       "    branch_time_in_child:            0.0\n",
+       "    branch_time_in_parent:           87658.0\n",
+       "    creation_date:                   2020-06-05T04:06:11Z\n",
+       "    ...                              ...\n",
+       "    variant_label:                   r10i1p1f1\n",
+       "    version:                         v20200605\n",
+       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
+       "    cmor_version:                    3.4.0\n",
+       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1ab58bcf-50ec-4ceb-ab51-32d693d67b6a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1ab58bcf-50ec-4ceb-ab51-32d693d67b6a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e58c46f0-2f6d-48ba-a306-3d98d6aa4172' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e58c46f0-2f6d-48ba-a306-3d98d6aa4172' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1870-01-16 12:00:00 ... 1874-12-...</div><input id='attrs-025c54b5-a966-4581-9e05-35ea01a06f7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-025c54b5-a966-4581-9e05-35ea01a06f7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d31f73-4db8-4ec8-8a06-f8e35c0d00c6' class='xr-var-data-in' type='checkbox'><label for='data-b3d31f73-4db8-4ec8-8a06-f8e35c0d00c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1870, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1871, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 2, 15, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1872, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1873, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(1874, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-9ee7d84e-0db3-45dc-8332-61d8a68bf9cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ee7d84e-0db3-45dc-8332-61d8a68bf9cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007373be-23a8-4682-beaa-7589957e3d95' class='xr-var-data-in' type='checkbox'><label for='data-007373be-23a8-4682-beaa-7589957e3d95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
+       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
+       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
+       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
+       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
+       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
+       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
+       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
+       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
+       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
+       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
+       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
+       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
+       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
+       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
+       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
+       "        90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-e781833f-0d30-4365-a591-54cf75f79ea9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e781833f-0d30-4365-a591-54cf75f79ea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dda9a38-953d-4a6c-be40-30079105c453' class='xr-var-data-in' type='checkbox'><label for='data-2dda9a38-953d-4a6c-be40-30079105c453' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
+       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
+       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
+       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
+       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
+       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
+       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
+       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
+       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
+       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
+       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
+       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
+       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
+       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
+       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
+       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
+       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
+       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
+       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
+       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
+       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
+       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
+       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
+       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-db24a522-7d1c-4dfd-baa7-e7e941815fbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db24a522-7d1c-4dfd-baa7-e7e941815fbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4e15345-68b0-4595-abbf-290ae5fccda0' class='xr-var-data-in' type='checkbox'><label for='data-a4e15345-68b0-4595-abbf-290ae5fccda0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a16b23f5-4a37-4802-af42-3b97fc6b5b27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a16b23f5-4a37-4802-af42-3b97fc6b5b27' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e4f7c32-92fd-4fd9-969b-262276c86ada' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e4f7c32-92fd-4fd9-969b-262276c86ada' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9930d2a6-10b7-41ce-8b1c-67dc2b3b892e' class='xr-var-data-in' type='checkbox'><label for='data-9930d2a6-10b7-41ce-8b1c-67dc2b3b892e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><pre>[120 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52380a32-adc6-4cba-b694-12e8b4cc6341' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52380a32-adc6-4cba-b694-12e8b4cc6341' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cc94ee5-c464-470a-8398-75e3118ae311' class='xr-var-data-in' type='checkbox'><label for='data-1cc94ee5-c464-470a-8398-75e3118ae311' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><pre>[290 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b737a50f-f81b-4439-942a-e0a5d807fde7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b737a50f-f81b-4439-942a-e0a5d807fde7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8377db34-cd4e-49c0-8913-a39d2e0283c0' class='xr-var-data-in' type='checkbox'><label for='data-8377db34-cd4e-49c0-8913-a39d2e0283c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b009238e-bb07-4086-830c-2fded04e700d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b009238e-bb07-4086-830c-2fded04e700d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee539eb7-e645-4e59-8d20-6a78b140a525' class='xr-var-data-in' type='checkbox'><label for='data-ee539eb7-e645-4e59-8d20-6a78b140a525' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:10Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2020-06-05T04:06:10Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 145 192]</dd></dl></div><div class='xr-var-data'><pre>[1670400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b647d0d-e75e-4962-91a7-6ebf9b04ea9d' class='xr-section-summary-in' type='checkbox'  ><label for='section-5b647d0d-e75e-4962-91a7-6ebf9b04ea9d' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
+       "aerosol: CLASSIC (v1.0)\n",
+       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
+       "atmosChem: none\n",
+       "land: CABLE2.4\n",
+       "landIce: none\n",
+       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
+       "ocnBgchem: WOMBAT (same grid as ocean)\n",
+       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20200605</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 7MB\n",
+       "Dimensions:    (time: 60, bnds: 2, lat: 145, lon: 192)\n",
+       "Coordinates:\n",
+       "  * time       (time) object 480B 1870-01-16 12:00:00 ... 1874-12-16 12:00:00\n",
+       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
+       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
+       "    height     float64 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) object 960B ...\n",
+       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
+       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
+       "    tas        (time, lat, lon) float32 7MB ...\n",
+       "Attributes: (12/48)\n",
+       "    Conventions:                     CF-1.7 CMIP-6.2\n",
+       "    activity_id:                     CMIP\n",
+       "    branch_method:                   standard\n",
+       "    branch_time_in_child:            0.0\n",
+       "    branch_time_in_parent:           87658.0\n",
+       "    creation_date:                   2020-06-05T04:06:11Z\n",
+       "    ...                              ...\n",
+       "    variant_label:                   r10i1p1f1\n",
+       "    version:                         v20200605\n",
+       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
+       "    cmor_version:                    3.4.0\n",
+       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  time"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# parameters\n",
+    "import xcdat as xc\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# open dataset\n",
+    "ds = xc.tutorial.open_dataset(\"tas_amon_access\", use_cftime=True)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4edc5a9-a098-4270-a169-57d6d519922b",
+   "metadata": {},
+   "source": [
+    "## 2. Masking land/sea\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90f2eb0e-8f5c-4186-b27f-40537e9a173a",
+   "metadata": {},
+   "source": [
+    "xCDAT supports two methods of generating land/sea mask.\n",
+    "\n",
+    "- `regionmask` - This method uses the [regionmask](https://regionmask.readthedocs.io/en/stable/) package to generate the mask.\n",
+    "- `pcmdi` - This method uses the PCMDI method developed by [Taylor and Doutriaux (2000)](https://pcmdi.llnl.gov/report/ab58.html) to generate the mask.\n",
+    "\n",
+    "The `regionmask` method uses the [1:100M Natrual Earth Land data](https://www.naturalearthdata.com/downloads/110m-physical-vectors/110m-land/) as the mask source.\n",
+    "\n",
+    "The `pcmdi` method uses the `navy_land.nc` dataset; a high resolution land/sea mask with fractional land values in an iterative refinement process to generate a highly accurate mask.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f7842cc1-8b0e-4d15-83db-fd9612b6ef5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = ds.spatial.mask_land(\"tas\")\n",
+    "\n",
+    "pcmdi_mask = ds.spatial.mask_land(\"tas\", method=\"pcmdi\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "154b72a7-129b-4927-a264-4c7c4159a3f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'PCMDI')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, figsize=(16, 4))\n",
+    "mask.isel(time=0).tas.plot(ax=axs[0])\n",
+    "axs[0].set_title(\"RegionMask\")\n",
+    "pcmdi_mask.isel(time=0).tas.plot(ax=axs[1])\n",
+    "axs[1].set_title(\"PCMDI\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9b25fbca-2245-477c-84d3-acb0bc15ec03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = ds.spatial.mask_sea(\"tas\")\n",
+    "\n",
+    "pcmdi_mask = ds.spatial.mask_sea(\"tas\", method=\"pcmdi\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1d07b7b6-9741-4e08-9a45-c6147776b858",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'PCMDI')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, figsize=(16, 4))\n",
+    "mask.isel(time=0).tas.plot(ax=axs[0])\n",
+    "axs[0].set_title(\"RegionMask\")\n",
+    "pcmdi_mask.isel(time=0).tas.plot(ax=axs[1])\n",
+    "axs[1].set_title(\"PCMDI\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca128698-efa1-4af2-8f3b-030e626badae",
+   "metadata": {},
+   "source": [
+    "## 3. Generating a land/sea mask\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5c3bf82d-87b4-46f5-92ae-8c8fdd49a2bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid = xc.create_uniform_grid(-90, 90, 1, 0, 359, 1)\n",
+    "\n",
+    "mask = grid.spatial.generate_land_sea_mask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8875c6a7-6eb7-4f71-b8ad-8fc6b8b8fe31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x30f60e210>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mask.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "534b5a87-a8c8-4a04-9321-0b3679a5c012",
+   "metadata": {},
+   "source": [
+    "## 4. Customizing land/sea mask\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0a2cc12-c5d7-4a71-8485-773553249010",
+   "metadata": {},
+   "source": [
+    "The first example customization is using a different fractional high resolution source. By default the `navy_land.nc` from [xcdat-data repo](https://github.com/xCDAT/xcdat-data) is used but it's possible to use a difference source.\n",
+    "\n",
+    "**This is just a toy example using high resolution noise.**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7ba04c90-147a-45dd-a02b-01eec4dc2962",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Custom Source')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "highres_ds = xc.create_uniform_grid(-90, 90, 1/6, 0, 359, 1/6)\n",
+    "highres_ds[\"frac\"] = ((\"lat\", \"lon\"), np.random.random((highres_ds.lat.shape[0], highres_ds.lon.shape[0])))\n",
+    "\n",
+    "mask = ds.spatial.mask_land(\"tas\", method=\"pcmdi\", source=highres_ds, source_data_var=\"frac\")\n",
+    "mask.tas.isel(time=0).plot()\n",
+    "plt.title(\"Custom Source\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3c29cbc-2d6c-42ea-b734-6eb2c8f6ddb4",
+   "metadata": {},
+   "source": [
+    "The next example changes the thresholds used to determine if a cell should be flipped from land to sea during the iterative mask refinement performed by the `pcmdi` method.\n",
+    "\n",
+    "**The default thresholds are 0.2 and 0.3.**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b8115c6b-1bc1-42fe-87dd-e8e25a43c0ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask1 = ds.spatial.mask_land(\"tas\", method=\"pcmdi\", threshold1=0.6, threshold2=0.8)\n",
+    "\n",
+    "mask2 = ds.spatial.mask_land(\"tas\", method=\"pcmdi\", threshold1=0.1, threshold2=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "297b1bba-7426-4b41-b7c0-aca58ad00609",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Lower')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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wAT1qBnoUehQAUDkQ7g0AABViyZIl7PPPP48db6oWDgAAAAAAAPQoACDNoJMSAAAAAAAAAAAAAABQU7K1XT0AAAAAAAAAAAAAACDtoJMSAAAAAAAAAAAAAABQU9BJCQAAAAAAAAAAAAAAqCnopAQAAAAAAAAAAAAAANQUdFICAAAAAAAAAAAAAABqCjopAQAAAAAAAAAAAAAANQWdlAAAAAAAAAAAAAAAAFZL/j+7QVHOasvV3wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1600x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, figsize=(16, 4))\n",
+    "mask1.isel(time=0).tas.plot(ax=axs[0])\n",
+    "axs[0].set_title(\"Higher\")\n",
+    "mask2.isel(time=0).tas.plot(ax=axs[1])\n",
+    "axs[1].set_title(\"Lower\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "xcdat_dev_576",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/gallery.rst b/docs/gallery.rst
index 5629ff456..b12bf0842 100644
--- a/docs/gallery.rst
+++ b/docs/gallery.rst
@@ -12,6 +12,7 @@ This gallery demonstrates how to use some of the features in ``xcdat``. Contribu
     examples/introduction-to-xcdat.ipynb
     examples/general-utilities.ipynb
     examples/spatial-average.ipynb
+    examples/spatial-landsea-mask.ipynb
     examples/temporal-average.ipynb
     examples/climatology-and-departures.ipynb
     examples/regridding-horizontal.ipynb
diff --git a/docs/gallery.yml b/docs/gallery.yml
index 3c83f2e57..5c0ff1251 100644
--- a/docs/gallery.yml
+++ b/docs/gallery.yml
@@ -10,6 +10,10 @@
   path: examples/spatial-average.ipynb
   thumbnail: _static/thumbnails/spatial-avg.png
 
+- title: Spatial Land/Sea Mask
+  path: examples/spatial-landsea-mask.ipynb
+  thumbnail: _static/thumbnails/spatial-landsea-mask.png
+
 - title: Temporal Averaging
   path: examples/temporal-average.ipynb
   thumbnail: _static/thumbnails/temporal-average.png
diff --git a/pyproject.toml b/pyproject.toml
index 618d87899..8bfbbe808 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,7 +28,9 @@ dependencies = [
   "netcdf4",
   "numpy >=2.0.0,<3.0.0",
   "pandas",
+  "pooch >=1.8",
   "python-dateutil",
+  "regionmask",
   "scipy",
   "sparse",
   "xarray >=2024.03.0",
@@ -52,7 +54,6 @@ docs = [
   "pandoc",
   "ipython",
   "gsw-xarray",
-  "pooch",
 ]
 dev = ["types-python-dateutil", "pre-commit", "ruff", "mypy"]
 
diff --git a/tests/test_data.py b/tests/test_data.py
new file mode 100644
index 000000000..dba597872
--- /dev/null
+++ b/tests/test_data.py
@@ -0,0 +1,47 @@
+import pytest
+
+import xcdat._data as data
+
+
+class TestGetPcmdiMaskPath:
+    def test_get_path_with_monkeypatch(self, tmp_path, monkeypatch):
+        """Simulate fetch without hitting network."""
+        fake_file = tmp_path / "navy_land.nc"
+        fake_file.write_bytes(b"dummy")
+
+        class DummyFetcher:
+            def fetch(self, rel):
+                return str(fake_file)
+
+        monkeypatch.setattr(data.pooch, "create", lambda **kwargs: DummyFetcher())
+
+        path = data._get_pcmdi_mask_path()
+
+        assert path.exists()
+        assert path.read_bytes() == b"dummy"
+
+    @pytest.mark.network
+    def test_get_path_with_real_fetch(self, tmp_path, monkeypatch):
+        """Test fetching the file from the network."""
+        # Override the XCDAT_DATA_DIR to use a temporary directory
+        monkeypatch.setenv("XCDAT_DATA_DIR", str(tmp_path))
+        path = data._get_pcmdi_mask_path()
+
+        assert path.exists()
+        assert path.stat().st_size > 1000
+
+    @pytest.mark.network
+    def test_get_path_from_cache(self, tmp_path, monkeypatch):
+        """Test fetching the file from the cache."""
+        # Override the XCDAT_DATA_DIR to use a temporary directory
+        monkeypatch.setenv("XCDAT_DATA_DIR", str(tmp_path))
+
+        # First fetch to ensure the file is downloaded
+        path = data._get_pcmdi_mask_path()
+        assert path.exists()
+        initial_mtime = path.stat().st_mtime
+
+        # Fetch again to ensure it uses the cached file
+        cached_path = data._get_pcmdi_mask_path()
+        assert cached_path.exists()
+        assert cached_path.stat().st_mtime == initial_mtime
diff --git a/tests/test_mask.py b/tests/test_mask.py
new file mode 100644
index 000000000..da506cef1
--- /dev/null
+++ b/tests/test_mask.py
@@ -0,0 +1,495 @@
+import sys
+from unittest import mock
+
+import numpy as np
+import pytest
+import xarray as xr
+
+from tests import fixtures
+from xcdat import mask
+from xcdat.regridder import grid
+
+np.set_printoptions(threshold=sys.maxsize, suppress=True)
+
+expected_land = [
+    [np.nan, np.nan, np.nan, np.nan],
+    [np.nan, np.nan, np.nan, np.nan],
+    [1, 1, 1, 1],
+    [1, 1, 1, 1],
+]
+
+expected_sea = [
+    [1, 1, 1, 1],
+    [1, 1, 1, 1],
+    [np.nan, np.nan, np.nan, np.nan],
+    [np.nan, np.nan, np.nan, np.nan],
+]
+
+
+@pytest.fixture(scope="function")
+def mask_da():
+    return xr.DataArray(
+        [
+            [1, 0, 0, 0],
+            [0, 0, 0, 0],
+            [0, 0, 1, 1],
+            [0, 0, 1, 1],
+        ],
+        dims=["lat", "lon"],
+    )
+
+
+@pytest.fixture(scope="function")
+def source_da():
+    return xr.DataArray(
+        [
+            [1, 0.4, 0, 0],
+            [0.5, 0.2, 0.4, 0.6],
+            [0, 0.2, 0.8, 1],
+            [0, 0.1, 1, 1],
+        ],
+        dims=["lat", "lon"],
+    )
+
+
+@pytest.fixture(scope="function")
+def diff_da():
+    return xr.DataArray(
+        [
+            [0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0],
+        ],
+        dims=["lat", "lon"],
+    )
+
+
+@pytest.fixture(scope="function")
+def ds():
+    return fixtures.generate_dataset(True, True, True)
+
+
+class TestMask:
+    def test_mask_land(self, ds):
+        expected = xr.DataArray(
+            expected_land,
+            dims=("lat", "lon"),
+            coords={
+                "lat": ds.lat.copy(),
+                "lon": ds.lon.copy(),
+                "time": ds.time[0].copy(),
+            },
+        )
+
+        output = ds.isel(time=0).spatial.mask_land("ts")
+
+        xr.testing.assert_allclose(output.ts, expected)
+
+    def test_mask_sea(self, ds):
+        expected = xr.DataArray(
+            expected_sea,
+            dims=("lat", "lon"),
+            coords={
+                "lat": ds.lat.copy(),
+                "lon": ds.lon.copy(),
+                "time": ds.time[0].copy(),
+            },
+        )
+
+        output = ds.isel(time=0).spatial.mask_sea("ts")
+
+        xr.testing.assert_allclose(output.ts, expected)
+
+    def test_generate_land_sea_mask(self, ds):
+        expected = xr.DataArray(
+            [
+                [1, 1, 1, 1],
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+                [0, 0, 0, 0],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+        )
+
+        output = ds.spatial.generate_land_sea_mask("ts")
+
+        xr.testing.assert_allclose(output, expected)
+
+    def test_generate_land_sea_mask_from_grid(self):
+        ds = grid.create_uniform_grid(-90, 90, 36, 0, 359, 32)
+
+        expected = xr.DataArray(
+            [
+                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
+                [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
+                [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
+                [0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1],
+                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+        )
+
+        output = ds.spatial.generate_land_sea_mask()
+
+        xr.testing.assert_allclose(output, expected)
+
+    def test_generate_land_sea_mask_missing_coordinate(self):
+        ds = grid.create_grid(x=grid.create_axis("lat", [x for x in range(10)]))
+
+        with pytest.raises(
+            KeyError,
+            match="Dataset is missing a required coordinate, ensure a lat and lon coordinate exist",
+        ):
+            ds.spatial.generate_land_sea_mask()
+
+
+class TestMaskGeneration:
+    def test_mask_invalid_data_var(self, ds):
+        with pytest.raises(KeyError):
+            mask.generate_and_apply_land_sea_mask(ds, "tas")
+
+    def test_mask_invalid_keep(self, ds):
+        with pytest.raises(
+            ValueError,
+            match=r"Keep value 'artic' is not valid, options are 'land, sea'",
+        ):
+            mask.generate_and_apply_land_sea_mask(ds, "ts", keep="artic")
+
+    def test_mask_output_mask(self, ds):
+        output = mask.generate_and_apply_land_sea_mask(ds, "ts", output_mask=True)
+
+        assert "ts_mask" in output
+
+        output = mask.generate_and_apply_land_sea_mask(ds, "ts", output_mask="sea_mask")
+
+        assert "sea_mask" in output
+
+    def test_mask_fractional(self, ds):
+        custom_mask = xr.DataArray(
+            [
+                [0.1, 0.1, 0.1, 0.1],
+                [0.1, 0.9, 0.9, 0.1],
+                [0.1, 0.9, 0.9, 0.1],
+                [0.1, 0.1, 0.1, 0.1],
+            ],
+            dims=("lat", "lon"),
+        )
+
+        expected_sea = xr.DataArray(
+            [
+                [1, 1, 1, 1],
+                [1, np.nan, np.nan, 1],
+                [1, np.nan, np.nan, 1],
+                [1, 1, 1, 1],
+            ],
+            dims=("lat", "lon"),
+            coords={
+                "lat": ds.lat.copy(),
+                "lon": ds.lon.copy(),
+                "time": ds.time.copy()[0],
+            },
+        )
+
+        output = mask.generate_and_apply_land_sea_mask(
+            ds.isel(time=0), "ts", mask=custom_mask
+        )
+
+        xr.testing.assert_allclose(output.ts, expected_sea)
+
+        # invert expected
+        expected_land = xr.where(expected_sea == 1, np.nan, expected_sea)
+        expected_land = xr.where(np.isnan(expected_sea), 1.0, np.nan)
+
+        output = mask.generate_and_apply_land_sea_mask(
+            ds.isel(time=0), "ts", keep="land", mask=custom_mask
+        )
+
+        xr.testing.assert_allclose(output.ts, expected_land)
+
+    def test_mask_custom(self, ds):
+        custom_mask = xr.DataArray(
+            [
+                [1, 0, 0, 1],
+                [1, 1, 1, 1],
+                [1, 1, 1, 1],
+                [1, 0, 0, 1],
+            ],
+            dims=("lat", "lon"),
+        )
+
+        expected = xr.DataArray(
+            [
+                [np.nan, 1, 1, np.nan],
+                [np.nan, np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan, np.nan],
+                [np.nan, 1, 1, np.nan],
+            ],
+            dims=("lat", "lon"),
+            coords={
+                "lat": ds.lat.copy(),
+                "lon": ds.lon.copy(),
+                "time": ds.time.copy()[0],
+            },
+        )
+
+        output = mask.generate_and_apply_land_sea_mask(
+            ds.isel(time=0), "ts", mask=custom_mask
+        )
+
+        xr.testing.assert_allclose(output.ts, expected)
+
+    def test_mask_land(self, ds):
+        expected = xr.DataArray(
+            expected_land,
+            dims=("lat", "lon"),
+            coords={
+                "lat": ds.lat.copy(),
+                "lon": ds.lon.copy(),
+                "time": ds.time[0].copy(),
+            },
+        )
+
+        output = mask.generate_and_apply_land_sea_mask(ds.isel(time=0), "ts")
+
+        xr.testing.assert_allclose(output.ts, expected)
+
+    def test_mask_sea(self, ds):
+        expected = xr.DataArray(
+            expected_sea,
+            dims=("lat", "lon"),
+            coords={
+                "lat": ds.lat.copy(),
+                "lon": ds.lon.copy(),
+                "time": ds.time[0].copy(),
+            },
+        )
+
+        output = mask.generate_and_apply_land_sea_mask(
+            ds.isel(time=0), "ts", keep="land"
+        )
+
+        xr.testing.assert_allclose(output.ts, expected)
+
+
+class TestLandSeaMask:
+    def test_generate_land_sea_mask_invalid_method(self, ds):
+        with pytest.raises(
+            ValueError,
+            match=r"Method value 'custom' is not valid, options are 'regionmask, pcmdi'",
+        ):
+            mask.generate_land_sea_mask(ds["ts"], method="custom")
+
+    def test_generate_land_sea_mask_regionmask(self, ds):
+        expected = xr.DataArray(
+            [
+                [1, 1, 1, 1],
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+                [0, 0, 0, 0],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+        )
+
+        output = mask.generate_land_sea_mask(ds["ts"])
+
+        xr.testing.assert_allclose(output, expected)
+
+    def test_generate_land_sea_mask_pcmdi(self, ds):
+        expected = xr.DataArray(
+            [
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+                [1, 1, 0, 1],
+                [0, 0, 0, 0],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+            attrs={"Conventions": "CF-1.0"},
+        )
+
+        output = mask.generate_land_sea_mask(ds["ts"], method="pcmdi")
+
+        xr.testing.assert_equal(output, expected)
+
+    def test_pcmdi_land_sea_mask_custom_source(self, ds):
+        source = xr.DataArray(
+            [
+                [0.1, 0.1, 0.9, 0.2],
+                [0.1, 0.9, 0.9, 0.1],
+                [0.0, 0.1, 0.9, 0.9],
+                [0.1, 0.1, 0.9, 0.1],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+            attrs={"Conventions": "CF-1.0"},
+        ).to_dataset(name="highres_mask")
+
+        output = mask.pcmdi_land_sea_mask(
+            ds["ts"], source=source, source_data_var="highres_mask"
+        )
+
+        expected = xr.DataArray(
+            [[0, 0, 1, 0], [0, 1, 1, 0], [0, 0, 1, 1], [0, 0, 1, 0]],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+            attrs={"Conventions": "CF-1.0"},
+        )
+
+        xr.testing.assert_allclose(output, expected)
+
+    def test_pcmdi_land_sea_mask_custom_source_error(self, ds):
+        source = xr.DataArray(
+            [
+                [0.1, 0.1, 0.9, 0.2],
+                [0.1, 0.9, 0.9, 0.1],
+                [0.0, 0.1, 0.9, 0.9],
+                [0.1, 0.1, 0.9, 0.1],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+            attrs={"Conventions": "CF-1.0"},
+        ).to_dataset(name="highres_mask")
+
+        with pytest.raises(
+            ValueError,
+            match="The 'source_data_var' value cannot be None when using the 'source' option.",
+        ):
+            mask.pcmdi_land_sea_mask(ds["ts"], source=source)
+
+    @mock.patch("xcdat.mask._improve_mask")
+    def test_pcmdi_land_sea_mask_multiple_iterations(self, _improve_mask, ds):
+        mask1 = xr.DataArray(
+            [
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+                [1, 1, 0, 1],
+                [0, 0, 0, 0],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+        )
+        mask2 = xr.DataArray(
+            [
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+        )
+
+        _improve_mask.side_effect = [
+            xr.Dataset({"sftlf": mask1.copy()}),
+            xr.Dataset({"sftlf": mask2.copy()}),
+            xr.Dataset({"sftlf": mask2.copy()}),
+        ]
+
+        expected = xr.DataArray(
+            [
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+                [1, 1, 1, 1],
+                [0, 0, 0, 0],
+            ],
+            dims=("lat", "lon"),
+            coords={"lat": ds.lat.copy(), "lon": ds.lon.copy()},
+        )
+
+        output = mask.pcmdi_land_sea_mask(ds["ts"])
+
+        xr.testing.assert_equal(output, expected)
+
+
+class TestUtilities:
+    def test_is_circular(self):
+        # Circular
+        lon = xr.DataArray(data=np.array([0, 90, 180, 270]), dims=["lon"])
+        lon_bnds = xr.DataArray(
+            data=np.array([[-45, 45], [45, 135], [135, 225], [225, 315]]),
+            dims=["lon", "bnds"],
+        )
+        assert mask._is_circular(lon, lon_bnds) is True
+
+        # Not circular
+        lon = xr.DataArray(data=np.array([0, 90, 180, 270]), dims=["lon"])
+        lon_bnds = xr.DataArray(
+            data=np.array([[-45, 45], [45, 135], [135, 225], [225, 300]]),
+            dims=["lon", "bnds"],
+        )
+        assert mask._is_circular(lon, lon_bnds) is False
+
+    def test_generate_surrounds_non_circular(self, source_da):
+        UL, UC, UR, ML, MR, LL, LC, LR = mask._generate_surrounds(
+            source_da, is_circular=False
+        )
+
+        np.testing.assert_array_equal(UC, source_da[2:, 1:-1])
+        np.testing.assert_array_equal(LC, source_da[:-2, 1:-1])
+        np.testing.assert_array_equal(ML, source_da[1:-1, :-2])
+        np.testing.assert_array_equal(MR, source_da[1:-1, 2:])
+        np.testing.assert_array_equal(UL, source_da[2:, :-2])
+        np.testing.assert_array_equal(UR, source_da[2:, 2:])
+        np.testing.assert_array_equal(LL, source_da[:-2, :-2])
+        np.testing.assert_array_equal(LR, source_da[:-2, 2:])
+
+    def test_generate_surrounds_circular(self, source_da):
+        UL, UC, UR, ML, MR, LL, LC, LR = mask._generate_surrounds(
+            source_da, is_circular=True
+        )
+
+        np.testing.assert_array_equal(UC, source_da[2:, :])
+        np.testing.assert_array_equal(LC, source_da[:-2, :])
+        np.testing.assert_array_equal(ML, np.roll(source_da[1:-1, :], 1, axis=1))
+        np.testing.assert_array_equal(MR, np.roll(source_da[1:-1, :], -1, axis=1))
+        np.testing.assert_array_equal(UL, np.roll(source_da[2:, :], 1, axis=1))
+        np.testing.assert_array_equal(UR, np.roll(source_da[2:, :], -1, axis=1))
+        np.testing.assert_array_equal(LL, np.roll(source_da[:-2, :], 1, axis=1))
+        np.testing.assert_array_equal(LR, np.roll(source_da[:-2, :], -1, axis=1))
+
+    def test_convert_points_to_land(self, mask_da, source_da, diff_da):
+        diff_da[1, 1] = 0.8
+
+        source_da[1, 1] = 0.4
+
+        surrounds = mask._generate_surrounds(source_da, is_circular=False)
+
+        result = mask._convert_points(
+            mask_da,
+            source_da,
+            diff_da,
+            threshold1=0.2,
+            threshold2=0.3,
+            is_circular=False,
+            surrounds=surrounds,
+            convert_land=True,
+        )
+        expected = mask_da.copy()
+        expected[1, 1] = 1.0
+        xr.testing.assert_allclose(result, expected)
+
+    def test_convert_points_to_sea(self, mask_da, source_da, diff_da):
+        diff_da[2, 2] = -0.8
+
+        source_da[2, 2] = 0.6
+
+        surrounds = mask._generate_surrounds(source_da, is_circular=False)
+
+        result = mask._convert_points(
+            mask_da,
+            source_da,
+            diff_da,
+            threshold1=-0.2,
+            threshold2=0.7,
+            is_circular=False,
+            surrounds=surrounds,
+            convert_land=False,
+        )
+        expected = mask_da.copy()
+        expected[2, 2] = 0.0
+        xr.testing.assert_allclose(result, expected)
diff --git a/xcdat/_data.py b/xcdat/_data.py
new file mode 100644
index 000000000..6d9c01005
--- /dev/null
+++ b/xcdat/_data.py
@@ -0,0 +1,54 @@
+from pathlib import Path
+
+import pooch
+
+from xcdat._logger import _setup_custom_logger
+
+BASE_URL = "https://raw.githubusercontent.com/xCDAT/xcdat-data/main/resources/"
+
+REGISTRY = {
+    "navy_land.nc": "sha256:652dc16af076ee2c407cc627ac4787f365ed5d49ca011daf3ded7a652a8c5fce",
+}
+
+logger = _setup_custom_logger(__name__)
+
+
+def _get_pcmdi_mask_path() -> Path:
+    """
+    Fetch and cache the canonical PCMDI land/sea mask from xcdat-data.
+
+    This function ensures that the PCMDI land/sea mask is always available by
+    downloading it from the xcdat-data repository and caching it locally. The
+    cache is managed automatically by pooch.
+
+    Caching behavior:
+        - Files are cached in the platform-specific cache directory
+          (e.g., ``~/.cache/xcdat`` on Linux/macOS,
+          ``%LOCALAPPDATA%\\xcdat\\Cache`` on Windows).
+        - The cache location can be overridden by setting the ``XCDAT_DATA_DIR``
+          environment variable.
+        - Integrity is guaranteed by verifying a SHA256 checksum.
+
+    For offline workflows, you can pre-download the mask with:
+
+    >>> from xcdat._data import _get_pcmdi_mask_path
+    >>> path = _get_pcmdi_mask_path()
+
+    Returns
+    -------
+    Path
+        The path to the cached PCMDI land/sea mask file.
+
+    References
+    ----------
+    - [1] xcdat-data repository: https://github.com/xCDAT/xcdat-data
+    """
+    fetcher = pooch.create(
+        path=pooch.os_cache("xcdat"),
+        base_url=BASE_URL,
+        registry=REGISTRY,
+        env="XCDAT_DATA_DIR",
+    )
+    filepath = fetcher.fetch("navy_land.nc")
+
+    return Path(filepath)
diff --git a/xcdat/mask.py b/xcdat/mask.py
new file mode 100644
index 000000000..44646b73f
--- /dev/null
+++ b/xcdat/mask.py
@@ -0,0 +1,585 @@
+from typing import Any, Callable
+
+import numpy as np
+import regionmask
+import xarray as xr
+
+from xcdat import open_dataset
+from xcdat._data import _get_pcmdi_mask_path
+from xcdat._logger import _setup_custom_logger
+from xcdat.axis import get_dim_coords
+from xcdat.regridder.accessor import _obj_to_grid_ds
+from xcdat.regridder.grid import create_grid
+
+logger = _setup_custom_logger(__name__)
+
+VALID_METHODS: list[str] = ["regionmask", "pcmdi"]
+VALID_KEEP: list[str] = ["land", "sea"]
+
+
+def generate_and_apply_land_sea_mask(
+    ds: xr.Dataset,
+    data_var: str,
+    method: str = "regionmask",
+    keep: str = "sea",
+    threshold: float | None = None,
+    mask: xr.DataArray | None = None,
+    output_mask: bool | str = False,
+    **options: Any,
+) -> xr.Dataset:
+    """Generate a land-sea mask and apply it to a data variable in a dataset.
+
+    Parameters
+    ----------
+    ds : xr.Dataset
+        The dataset to mask.
+    data_var : str
+        The key of the data variable to mask.
+    method : str, optional
+        The masking method, by default "regionmask".
+        Supported methods: "regionmask", "pcmdi".
+    keep : str, optional
+        Whether to keep "land" or "sea" points, by default "sea".
+    threshold : float | None, optional
+        The threshold used to determine cell classification. The default
+        value for this argument depends on `keep`. If `keep` is `sea` then
+        the default is 0.2 and values less than or equal will be considered
+        sea. If `keep` is `land` then the default is 0.8 and values greater
+        or equal will be considered land.
+    mask : xr.DataArray | None, optional
+        A custom mask to apply, by default None. If None, a mask is
+        generated using the specified ``method``.
+    **options : Any
+        These options are passed directly to the ``method``. See
+        :func:`xcdat.mask.pcmdi_land_sea_mask` for PCMDI options.
+
+    Returns
+    -------
+    xr.Dataset
+        The dataset with the masked data variable.
+
+    Raises
+    ------
+    ValueError
+        If `keep` is not "land" or "sea".
+
+    Examples
+    --------
+
+    Mask a data variable by land using the default method (regionmask):
+    >>> ds_masked = generate_mask(ds, "tas", keep="sea")
+
+    Mask a data variable by sea using the PCMDI method with custom threshold:
+    >>> ds_masked = generate_mask(ds, "tas", method="pcmdi", keep="land", threshold=0.7)
+
+    Mask a data variable by land using a custom mask and output the mask:
+    >>> custom_mask = xr.DataArray(...)  # Define your custom mask here
+    >>> ds_masked = generate_mask(ds, "tas", keep="sea", mask=custom_mask, output_mask=True)
+
+    Mask a data variable by sea and add the mask to the dataset with a custom name:
+    >>> ds_masked = generate_mask(ds, "tas", keep="land", output_mask="land_mask")
+    """
+    if keep not in VALID_KEEP:
+        raise ValueError(
+            f"Keep value {keep!r} is not valid, options are {', '.join(VALID_KEEP)!r}"
+        )
+
+    _ds = ds.copy()
+
+    da = _ds[data_var]
+
+    if mask is None:
+        mask = generate_land_sea_mask(da, method, **options)
+
+    if keep == "sea":
+        _ds[data_var] = da.where(mask <= (threshold or 0.2))
+    else:
+        _ds[data_var] = da.where(mask >= (threshold or 0.8))
+
+    if output_mask:
+        if isinstance(output_mask, str):
+            mask_name = output_mask
+        else:
+            mask_name = f"{data_var}_mask"
+
+        _ds[mask_name] = mask
+
+    return _ds
+
+
+def generate_land_sea_mask(
+    da: xr.DataArray, method: str = "regionmask", **options: Any
+) -> xr.DataArray:
+    """Generate a land-sea mask.
+
+    Parameters
+    ----------
+    da : xr.DataArray
+        The DataArray to generate the mask for.
+    method : str, optional
+        The method to use for generating the mask, by default "regionmask".
+        Supported methods: "regionmask", "pcmdi".
+    **options : Any
+        These options are passed directly to the ``method``. See specific
+        method documentation for available options:
+        :func:`pcmdi_land_sea_mask` for PCMDI options
+
+    Returns
+    -------
+    xr.DataArray
+        The land-sea mask.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not "regionmask" or "pcmdi".
+
+    References
+    ----------
+    .. _PCMDI's report #58: https://pcmdi.llnl.gov/report/ab58.html
+
+    History
+    -------
+    2023-06 The [original code](https://github.com/CDAT/cdutil/blob/master/
+    cdutil/create_landsea_mask.py) was rewritten using xarray and xcdat by Jiwoo Lee
+
+    Examples
+    --------
+
+    Generate a land-sea mask using the default method (regionmask):
+
+    >>> import xcdat
+    >>> ds = xcdat.open_dataset("/path/to/file")
+    >>> mask = xcdat.mask.generate_land_sea_mask(ds["tas"], method="regionmask")
+
+    Generate a land-sea mask using the PCMDI method with custom options:
+
+    >>> mask = xcdat.mask.generate_land_sea_mask(
+    ...     ds["tas"], method="pcmdi", threshold1=0.3, threshold2=0.4
+    ... )
+    """
+    if method not in VALID_METHODS:
+        raise ValueError(
+            f"Method value {method!r} is not valid, options are {', '.join(VALID_METHODS)!r}"
+        )
+
+    if method == "regionmask":
+        land_mask = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+
+        lon, lat = get_dim_coords(da, "X"), get_dim_coords(da, "Y")
+
+        land_sea_mask = land_mask.mask(lon, lat=lat)
+
+        land_sea_mask = xr.where(land_sea_mask, 0, 1)
+    elif method == "pcmdi":
+        land_sea_mask = pcmdi_land_sea_mask(da, **options)
+
+    return land_sea_mask
+
+
+def pcmdi_land_sea_mask(
+    da: xr.DataArray,
+    threshold1: float = 0.2,
+    threshold2: float = 0.3,
+    source: xr.Dataset | None = None,
+    source_data_var: str | None = None,
+) -> xr.DataArray:
+    """
+    Generate a land-sea mask using the PCMDI method.
+
+    This method uses a high-resolution land-sea mask and regrids it to the
+    resolution of the input DataArray. It then iteratively improves the mask
+    based on specified thresholds.
+
+    Parameters
+    ----------
+    da : xr.DataArray
+        The DataArray to generate the mask for.
+    threshold1 : float, optional
+        The first threshold for improving the mask, by default 0.2.
+    threshold2 : float, optional
+        The second threshold for improving the mask, by default 0.3.
+    source : xr.Dataset | None, optional
+        A custom Dataset containing the variable to use as the high-resolution
+        source. If not provided, a default high-resolution land-sea mask is used.
+    source_data_var : str | None, optional
+        The name of the variable in `source` to use as the high-resolution
+        source. If `source` is not provided, this defaults to "sftlf".
+
+    Returns
+    -------
+    xr.DataArray
+        The generated land-sea mask.
+
+    Raises
+    ------
+    ValueError
+        If `source` is provided but `source_data_var` is None.
+
+    Notes
+    -----
+    By default, the ``navy_land.nc`` file is used as the high-resolution land–sea
+    mask. This file is distributed by the [1]_ PCMDI (Program for Climate Model
+    Diagnosis and Intercomparison) Metrics Package, and is derived from the U.S.
+    Navy 1/6° land–sea mask dataset.
+
+    If ``source`` is not provided, the ``navy_land.nc`` file is automatically
+    downloaded and cached from the `xcdat-data` repository:
+    https://github.com/xCDAT/xcdat-data.
+
+    For more information on caching behavior, refer to the
+    :py:func:`xcdat._data._get_pcmdi_mask_path()` function.
+
+    References
+    ----------
+    .. [1] https://github.com/PCMDI/pcmdi_metrics/blob/main/
+
+    Examples
+    --------
+    Generate a land-sea mask using the PCMDI method:
+
+    >>> import xcdat
+    >>> ds = xcdat.open_dataset("/path/to/file")
+    >>> land_sea_mask = xcdat.mask.pcmdi_land_sea_mask(ds["tas"])
+
+    Generate a land-sea mask using the PCMDI method with custom thresholds:
+
+    >>> land_sea_mask = xcdat.mask.pcmdi_land_sea_mask(
+    ...     ds["tas"], threshold1=0.3, threshold2=0.4
+    ... )
+
+    Generate a land-sea mask using the PCMDI method with a custom high-res source:
+
+    >>> highres_ds = xcdat.open_dataset("/path/to/file")
+    >>> land_sea_mask = xcdat.mask.pcmdi_land_sea_mask(
+    ...     ds["tas"], source=highres_ds, source_data_var="highres"
+    ... )
+
+    For offline workflows, you can pre-download the mask with:
+
+    >>> from xcdat._data import _get_pcmdi_mask_path
+    >>> path = _get_pcmdi_mask_path()
+    """
+    if source is not None and source_data_var is None:
+        raise ValueError(
+            "The 'source_data_var' value cannot be None when using the 'source' option."
+        )
+
+    if source is None:
+        source_data_var = "sftlf"
+
+        resource_path = _get_pcmdi_mask_path()
+
+        # Turn off time decoding to prevent logger warning since this dataset
+        # does not have a time axis.
+        source = open_dataset(resource_path, decode_times=False)
+
+    source_regrid = source.regridder.horizontal(
+        source_data_var, _obj_to_grid_ds(da), tool="regrid2"
+    )
+
+    mask = source_regrid.copy()
+    mask[source_data_var] = xr.where(source_regrid[source_data_var] > 0.5, 1, 0).astype(
+        "i"
+    )
+
+    lon = mask[source_data_var].cf["X"]
+    lon_bnds = mask.bounds.get_bounds("X")
+    is_circular = _is_circular(lon, lon_bnds)
+
+    surrounds = _generate_surrounds(mask[source_data_var], is_circular)
+
+    i = 0
+
+    while i <= 25:
+        logger.debug("Iteration %i", i + 1)
+
+        improved_mask = _improve_mask(
+            mask.copy(deep=True),
+            source_regrid,
+            source_data_var,  # type: ignore[arg-type]
+            surrounds,
+            is_circular,
+            threshold1,
+            threshold2,
+        )
+
+        if improved_mask.equals(mask):
+            break
+
+        mask = improved_mask
+
+        i += 1
+
+    return mask[source_data_var]
+
+
+def _is_circular(lon: xr.DataArray, lon_bnds: xr.DataArray) -> bool:
+    """Check if a longitude axis is circular.
+
+    Parameters
+    ----------
+    lon : xr.DataArray
+        The longitude coordinates.
+    lon_bnds : xr.DataArray
+        The longitude bounds.
+
+    Returns
+    -------
+    bool
+        True if the longitude axis is circular, False otherwise.
+    """
+    axis_start, axis_stop = float(lon[0]), float(lon[-1])
+    delta = float(lon[-1] - lon[-2])
+    alignment = abs(axis_stop + delta - axis_start - 360.0)
+    tolerance = 0.01 * delta
+    mod_360 = float(lon_bnds[-1][1] - lon_bnds[0][0]) % 360
+
+    return alignment < tolerance and mod_360 == 0
+
+
+def _improve_mask(
+    mask: xr.Dataset,
+    source: xr.Dataset,
+    data_var: str,
+    surrounds: list[np.ndarray],
+    is_circular: bool,
+    threshold1=0.2,
+    threshold2=0.3,
+) -> xr.Dataset:
+    """Improve a land-sea mask.
+
+    This function improves a land-sea mask by converting points based on
+    their surrounding values and a source mask.
+
+    It is useful for enhancing the accuracy of land-sea masks, which are often
+    used in climate modeling and geospatial analysis. By considering surrounding
+    points and thresholds, it ensures smoother transitions and corrects
+    discrepancies between the mask and the source dataset.
+
+    Parameters
+    ----------
+    mask : xr.Dataset
+        The mask to improve.
+    source : xr.Dataset
+        The source dataset for comparison.
+    data_var : str
+        The name of the data variable in the mask and source.
+    surrounds : list[np.ndarray]
+        A list of surrounding points for each point in the mask.
+    is_circular : bool
+        Whether the longitude axis is circular.
+    threshold1 : float, optional
+        The first threshold for conversion, by default 0.2.
+    threshold2 : float, optional
+        The second threshold for conversion, by default 0.3.
+
+    Returns
+    -------
+    xr.Dataset
+        The improved mask.
+    """
+    mask_approx = _map2four(
+        mask,
+        data_var,
+    )
+
+    diff = source[data_var] - mask_approx[data_var]
+
+    mask_convert_land = _convert_points(
+        mask[data_var] * 1.0,
+        source[data_var],
+        diff,
+        threshold1,
+        threshold2,
+        is_circular,
+        surrounds,
+    )
+
+    mask_convert_sea = _convert_points(
+        mask_convert_land,
+        source[data_var],
+        diff,
+        -threshold1,
+        1.0 - threshold2,
+        is_circular,
+        surrounds,
+        convert_land=False,
+    )
+
+    mask[data_var] = mask_convert_sea.astype("i")
+
+    return mask
+
+
+def _map2four(mask: xr.Dataset, data_var: str) -> xr.Dataset:
+    """Map a mask to four subgrids and back.
+
+    This function regrids a mask to four subgrids (odd-odd, odd-even,
+    even-odd, even-even) and then combines them back into a single mask.
+    This is used to approximate the mask at a higher resolution.
+
+    Parameters
+    ----------
+    mask : xr.Dataset
+        The mask to process.
+    data_var : str
+        The name of the data variable in the mask.
+
+    Returns
+    -------
+    xr.Dataset
+        The processed mask.
+    """
+    mask_temp = mask.copy()
+
+    lat, lon = mask_temp[data_var].cf["Y"], mask_temp[data_var].cf["X"]
+    lat_odd, lat_even = lat[::2], lat[1::2]
+    lon_odd, lon_even = lon[::2], lon[1::2]
+
+    odd_odd = create_grid(y=lat_odd, x=lon_odd, add_bounds=True)
+    odd_even = create_grid(y=lat_odd, x=lon_even, add_bounds=True)
+    even_odd = create_grid(y=lat_even, x=lon_odd, add_bounds=True)
+    even_even = create_grid(y=lat_even, x=lon_even, add_bounds=True)
+
+    regrid_odd_odd = mask_temp.regridder.horizontal(data_var, odd_odd, tool="regrid2")
+    regrid_odd_even = mask_temp.regridder.horizontal(data_var, odd_even, tool="regrid2")
+    regrid_even_odd = mask_temp.regridder.horizontal(data_var, even_odd, tool="regrid2")
+    regrid_even_even = mask_temp.regridder.horizontal(
+        data_var, even_even, tool="regrid2"
+    )
+
+    output = np.zeros(mask_temp[data_var].shape, dtype="f")
+
+    output[::2, ::2] = regrid_odd_odd[data_var].data
+    output[::2, 1::2] = regrid_odd_even[data_var].data
+    output[1::2, ::2] = regrid_even_odd[data_var].data
+    output[1::2, 1::2] = regrid_even_even[data_var].data
+
+    mask_temp[data_var] = (mask_temp[data_var].dims, output)
+
+    return mask_temp
+
+
+def _convert_points(
+    mask: xr.DataArray,
+    source: xr.DataArray,
+    diff: xr.DataArray,
+    threshold1: float,
+    threshold2: float,
+    is_circular: bool,
+    surrounds: list[np.ndarray],
+    convert_land=True,
+) -> xr.DataArray:
+    """Convert points in a mask based on surrounding values.
+
+    This function converts points in a mask from land to sea or sea to land
+    based on a set of thresholds and the values of surrounding points.
+
+    Parameters
+    ----------
+    mask : xr.DataArray
+        The mask to modify.
+    source : xr.DataArray
+        The source data for comparison.
+    diff : xr.DataArray
+        The difference between the source and an approximated mask.
+    threshold1 : float
+        Threshold for points in the `diff` DataArray.
+    threshold2 : float
+        Threshold for points in the `source` DataArray.
+    is_circular : bool
+        Whether the longitude axis is circular.
+    surrounds : list[np.ndarray]
+        A list of surrounding points for each point in the mask.
+    convert_land : bool, optional
+        Whether to convert points to land (True) or sea (False), by default True.
+
+    Returns
+    -------
+    xr.DataArray
+        The modified mask.
+    """
+    UL, UC, UR, ML, MR, LL, LC, LR = surrounds
+
+    mask_value = 1.0
+    compare_func: Callable
+    if convert_land:
+        compare_func = np.greater
+    else:
+        compare_func = np.less
+        mask_value = 0.0
+
+    flip_value = abs(mask_value - 1.0)
+
+    c1 = compare_func(diff, threshold1)
+    c2 = compare_func(source, threshold2)
+    c = np.logical_and(c1, c2)
+
+    cUL, cUC, cUR, cML, cMR, cLL, cLC, cLR = _generate_surrounds(c, is_circular)
+
+    if is_circular:
+        c = c[1:-1]
+        temp = source.data[1:-1]
+    else:
+        c = c[1:-1, 1:-1]
+        temp = source.data[1:-1, 1:-1]
+
+    m = np.logical_and(c, compare_func(temp, np.where(cUL, UL, flip_value)))
+    m = np.logical_and(m, compare_func(temp, np.where(cUC, UC, flip_value)))
+    m = np.logical_and(m, compare_func(temp, np.where(cUR, UR, flip_value)))
+    m = np.logical_and(m, compare_func(temp, np.where(cML, ML, flip_value)))
+    m = np.logical_and(m, compare_func(temp, np.where(cMR, MR, flip_value)))
+    m = np.logical_and(m, compare_func(temp, np.where(cLL, LL, flip_value)))
+    m = np.logical_and(m, compare_func(temp, np.where(cLC, LC, flip_value)))
+    m = np.logical_and(m, compare_func(temp, np.where(cLR, LR, flip_value)))
+
+    if is_circular:
+        mask[1:-1] = xr.where(m, mask_value, mask[1:-1])
+    else:
+        mask[1:-1, 1:-1] = xr.where(m, mask_value, mask[1:-1, 1:-1])
+
+    return mask
+
+
+def _generate_surrounds(da: xr.DataArray, is_circular: bool) -> list[np.ndarray]:
+    """Generate surrounding points for each point in a DataArray.
+
+    This function returns a list of 8 arrays, each representing the
+    values of the 8 surrounding points (UL, UC, UR, ML, MR, LL, LC, LR) for each
+    point in the input DataArray.
+
+    Parameters
+    ----------
+    da : xr.DataArray
+        The input DataArray.
+    is_circular : bool
+        Whether the longitude axis is circular.
+
+    Returns
+    -------
+    list[np.ndarray]
+        A list of 8 arrays representing the surrounding points.
+    """
+    data = da.data
+
+    y_up, y_mid, y_down = slice(2, None), slice(1, -1), slice(None, -2)
+
+    if is_circular:
+        # For circular longitude, roll the horizontal axis.
+        UC, LC = data[y_up, :], data[y_down, :]
+        ML, MR = np.roll(data[y_mid, :], 1, axis=1), np.roll(data[y_mid, :], -1, axis=1)
+        UL, UR = np.roll(data[y_up, :], 1, axis=1), np.roll(data[y_up, :], -1, axis=1)
+        LL, LR = (
+            np.roll(data[y_down, :], 1, axis=1),
+            np.roll(data[y_down, :], -1, axis=1),
+        )
+    else:
+        # For non-circular, slice the horizontal axis.
+        x_left, x_mid, x_right = slice(None, -2), slice(1, -1), slice(2, None)
+        UC, LC = data[y_up, x_mid], data[y_down, x_mid]
+        ML, MR = data[y_mid, x_left], data[y_mid, x_right]
+        UL, UR = data[y_up, x_left], data[y_up, x_right]
+        LL, LR = data[y_down, x_left], data[y_down, x_right]
+
+    return [UL, UC, UR, ML, MR, LL, LC, LR]
diff --git a/xcdat/regridder/accessor.py b/xcdat/regridder/accessor.py
index d7306b1a0..40062aedd 100644
--- a/xcdat/regridder/accessor.py
+++ b/xcdat/regridder/accessor.py
@@ -3,6 +3,7 @@
 import xarray as xr
 
 from xcdat.axis import CFAxisKey, get_coords_by_name, get_dim_coords
+from xcdat.bounds import create_bounds
 from xcdat.regridder import regrid2, xesmf, xgcm
 from xcdat.regridder.grid import _validate_grid_has_single_axis_dim
 
@@ -79,69 +80,7 @@ def grid(self) -> xr.Dataset:
 
         >>> grid = ds.regridder.grid
         """
-        axis_names: list[CFAxisKey] = ["X", "Y", "Z"]
-
-        axis_coords: dict[str, xr.DataArray] = {}
-        axis_bounds: dict[str, xr.DataArray] = {}
-        axis_has_bounds: dict[CFAxisKey, bool] = {}
-
-        with xr.set_options(keep_attrs=True):
-            for axis in axis_names:
-                coord, bounds = self._get_axis_coord_and_bounds(axis)
-
-                if coord is not None:
-                    axis_coords[str(coord.name)] = coord
-
-                    if bounds is not None:
-                        axis_bounds[str(bounds.name)] = bounds
-                        axis_has_bounds[axis] = True
-                    else:
-                        axis_has_bounds[axis] = False
-
-        # Create a new dataset with coordinates and bounds
-        ds = xr.Dataset(
-            coords=axis_coords,
-            data_vars=axis_bounds,
-            attrs=self._ds.attrs,
-        )
-
-        # Add bounds only for axes that do not already have them. This
-        # prevents multiple sets of bounds being added for the same axis.
-        # For example, curvilinear grids can have multiple coordinates for the
-        # same axis (e.g., (nlat, lat) for X and (nlon, lon) for Y). We only
-        # need lat_bnds and lon_bnds for the X and Y axes, respectively, and not
-        # nlat_bnds and nlon_bnds.
-        for axis, has_bounds in axis_has_bounds.items():
-            if not has_bounds:
-                ds = ds.bounds.add_bounds(axis=axis)
-
-        return ds
-
-    def _get_axis_coord_and_bounds(
-        self, axis: CFAxisKey
-    ) -> tuple[xr.DataArray | None, xr.DataArray | None]:
-        try:
-            coord_var = get_coords_by_name(self._ds, axis)
-            if coord_var.size == 1:
-                raise ValueError(
-                    f"Coordinate '{coord_var}' is a singleton and cannot be used."
-                )
-        except (ValueError, KeyError):
-            try:
-                coord_var = get_dim_coords(self._ds, axis)  # type: ignore
-                _validate_grid_has_single_axis_dim(axis, coord_var)
-            except KeyError:
-                coord_var = None
-
-        if coord_var is None:
-            return None, None
-
-        bounds_var = None
-        bounds_key = coord_var.attrs.get("bounds")
-        if bounds_key:
-            bounds_var = self._ds.get(bounds_key)
-
-        return coord_var, bounds_var
+        return _obj_to_grid_ds(self._ds)
 
     def horizontal(
         self,
@@ -310,6 +249,104 @@ def vertical(
         return output_ds
 
 
+def _obj_to_grid_ds(obj: xr.Dataset | xr.DataArray) -> xr.Dataset:
+    """
+    Convert an xarray object to a new Dataset containing axis coordinates and
+    bounds.
+
+    This function extracts axis coordinates and bounds for the specified
+    axes ("X", "Y", "Z") from the input object and creates a new xarray
+    Dataset. If bounds are missing for an axis, they are added to the
+    output Dataset.
+
+    Parameters
+    ----------
+    obj : xr.Dataset or xr.DataArray
+        The input xarray object containing the data and attributes.
+
+    Returns
+    -------
+    xr.Dataset
+        A new xarray Dataset containing the axis coordinates, bounds, and
+        attributes from the input object.
+
+    Notes
+    -----
+    - The function ensures that bounds are only added for axes that do not
+      already have them. This avoids duplicating bounds for axes with
+      multiple coordinates (e.g., curvilinear grids).
+    - The `xr.set_options(keep_attrs=True)` context is used to preserve
+      attributes from the input object in the output Dataset.
+    """
+    axis_names: list[CFAxisKey] = ["X", "Y", "Z"]
+
+    axis_coords: dict[str, xr.DataArray] = {}
+    axis_bounds: dict[str, xr.DataArray] = {}
+    axis_has_bounds: dict[CFAxisKey, bool] = {}
+
+    with xr.set_options(keep_attrs=True):
+        for axis in axis_names:
+            coord, bounds = _get_axis_coord_and_bounds(obj, axis)
+
+            if coord is not None:
+                axis_coords[str(coord.name)] = coord
+
+                if bounds is not None:
+                    axis_bounds[str(bounds.name)] = bounds
+                    axis_has_bounds[axis] = True
+                else:
+                    axis_has_bounds[axis] = False
+
+    # Create a new dataset with coordinates and bounds
+    output_ds = xr.Dataset(
+        coords=axis_coords,
+        data_vars=axis_bounds,
+        attrs=obj.attrs,
+    )
+
+    # Add bounds only for axes that do not already have them. This
+    # prevents multiple sets of bounds being added for the same axis.
+    # For example, curvilinear grids can have multiple coordinates for the
+    # same axis (e.g., (nlat, lat) for X and (nlon, lon) for Y). We only
+    # need lat_bnds and lon_bnds for the X and Y axes, respectively, and not
+    # nlat_bnds and nlon_bnds.
+    for axis, has_bounds in axis_has_bounds.items():
+        if not has_bounds:
+            output_ds = output_ds.bounds.add_bounds(axis=axis)
+
+    return output_ds
+
+
+def _get_axis_coord_and_bounds(
+    obj: xr.Dataset | xr.DataArray, axis: CFAxisKey
+) -> tuple[xr.DataArray | None, xr.DataArray | None]:
+    try:
+        coord_var = get_coords_by_name(obj, axis)
+        if coord_var.size == 1:
+            raise ValueError(
+                f"Coordinate '{coord_var}' is a singleton and cannot be used."
+            )
+    except (ValueError, KeyError):
+        try:
+            coord_var = get_dim_coords(obj, axis)  # type: ignore
+            _validate_grid_has_single_axis_dim(axis, coord_var)
+        except KeyError:
+            coord_var = None
+
+    if coord_var is None:
+        return None, None
+
+    bounds_var = None
+    bounds_key = coord_var.attrs.get("bounds")
+    if bounds_key:
+        try:
+            bounds_var = obj.get(bounds_key)
+        except AttributeError:
+            bounds_var = create_bounds(axis, coord_var)
+
+    return coord_var, bounds_var
+
+
 def _get_input_grid(ds: xr.Dataset, data_var: str, dup_check_dims: list[CFAxisKey]):
     """
     Extract the grid from ``ds``.
diff --git a/xcdat/regridder/grid.py b/xcdat/regridder/grid.py
index 7fc9ebeb2..0c9279eac 100644
--- a/xcdat/regridder/grid.py
+++ b/xcdat/regridder/grid.py
@@ -118,7 +118,7 @@ def _create_gaussian_axis(nlats: int) -> tuple[xr.DataArray, xr.DataArray]:
 
     points, weights = _gaussian_axis(mid, nlats)
 
-    bounds = np.zeros((nlats + 1))
+    bounds = np.zeros((nlats + 1), dtype="float32")
     bounds[0], bounds[-1] = 1.0, -1.0
 
     for i in range(1, mid + 1):
@@ -440,6 +440,7 @@ def create_grid(
     y: xr.DataArray | tuple[xr.DataArray, xr.DataArray | None] | None = None,
     z: xr.DataArray | tuple[xr.DataArray, xr.DataArray | None] | None = None,
     attrs: dict[str, str] | None = None,
+    add_bounds: bool = False,
 ) -> xr.Dataset:
     """Creates a grid dataset using the specified axes.
 
@@ -530,6 +531,11 @@ def create_grid(
 
         ds = ds.assign_coords({coords.name: coords})
 
+    if add_bounds:
+        ds = ds.bounds.add_missing_bounds(
+            axes=[x.upper() for x, y in axes.items() if y is not None]
+        )
+
     return ds
 
 
diff --git a/xcdat/spatial.py b/xcdat/spatial.py
index 50dd4dc2f..fcc5b95de 100644
--- a/xcdat/spatial.py
+++ b/xcdat/spatial.py
@@ -2,7 +2,7 @@
 
 from collections.abc import Callable, Hashable
 from functools import reduce
-from typing import Literal, TypedDict, get_args
+from typing import Any, Literal, TypedDict, get_args
 
 import cf_xarray  # noqa: F401
 import numpy as np
@@ -15,6 +15,10 @@
     get_dim_keys,
 )
 from xcdat.dataset import _get_data_var
+from xcdat.mask import (
+    generate_and_apply_land_sea_mask,
+    generate_land_sea_mask,
+)
 from xcdat.utils import (
     _get_masked_weights,
     _if_multidim_dask_array_then_load,
@@ -60,6 +64,213 @@ class SpatialAccessor:
     def __init__(self, dataset: xr.Dataset):
         self._dataset: xr.Dataset = dataset
 
+    def mask_land(
+        self,
+        data_var: str,
+        method: str = "regionmask",
+        threshold: float | None = None,
+        mask: xr.DataArray | None = None,
+        output_mask: bool | str = False,
+        **options: Any,
+    ):
+        """
+        Masks a data variable by land.
+
+        Parameters
+        ----------
+        data_var : str
+            The key of the data variable to mask.
+        method : str, optional
+            The masking method, by default "regionmask".
+            Supported methods: "regionmask", "pcmdi".
+        threshold : float | None, optional
+            The threshold used to determine cell classification, values below
+            or equal to this are considered sea, defaults to 0.2.
+        mask : xr.DataArray | None, optional
+            A custom mask to apply, by default None. If None, a mask is
+            generated using the specified ``method``.
+        output_mask : bool | str, optional
+            If True, returns the mask as a DataArray along with the masked
+            dataset. If a string, the name of the mask variable to add to the
+            dataset. By default False.
+        **options : Any
+            These options are passed directly to the ``method``. See specific
+            method documentation for available options:
+            :func:`xcdat.mask.pcmdi_land_sea_mask` for PCMDI options.
+
+        Returns
+        -------
+        xr.Dataset
+            The dataset with the data variable masked by land.
+
+        Examples
+        --------
+
+        Mask a data variable by land using the default method (regionmask):
+
+        >>> ds_masked = ds.spatial.mask_land("tas")
+
+        Mask a data variable by land using the PCMDI method with custom threshold:
+
+        >>> ds_masked = ds.spatial.mask_land("tas", method="pcmdi", threshold=0.3)
+
+        Mask a data variable by land using a custom mask and output the mask:
+
+        >>> custom_mask = xr.DataArray(...)  # Define your custom mask here
+        >>> ds_masked = ds.spatial.mask_land("tas", mask=custom_mask, output_mask=True)
+
+        Mask a data variable by land and add the mask to the dataset with a custom name:
+
+        >>> ds_masked = ds.spatial.mask_land("tas", output_mask="land_mask")
+        """
+        return generate_and_apply_land_sea_mask(
+            self._dataset,
+            data_var,
+            method,
+            keep="sea",
+            threshold=threshold,
+            mask=mask,
+            output_mask=output_mask,
+            **options,
+        )
+
+    def mask_sea(
+        self,
+        data_var: str,
+        method: str = "regionmask",
+        threshold: float | None = None,
+        mask: xr.DataArray | None = None,
+        output_mask: bool | str = False,
+        **options: Any,
+    ):
+        """
+        Masks a data variable by sea.
+
+        Parameters
+        ----------
+        data_var : str
+            The key of the data variable to mask.
+        method : str, optional
+            The masking method, by default "regionmask".
+            Supported methods: "regionmask", "pcmdi".
+        threshold : float | None, optional
+            The threshold used to determine cell classification, values above
+            or equal to this are considered land, defaults to 0.8.
+        mask : xr.DataArray | None, optional
+            A custom mask to apply, by default None. If None, a mask is
+            generated using the specified ``method``.
+        output_mask : bool | str, optional
+            If True, returns the mask as a DataArray along with the masked
+            dataset. If a string, the name of the mask variable to add to the
+            dataset. By default False.
+        **options : Any
+            These options are passed directly to the ``method``. See specific
+            method documentation for available options:
+            :func:`xcdat.mask.pcmdi_land_sea_mask` for PCMDI options.
+
+        Returns
+        -------
+        xr.Dataset
+            The dataset with the data variable masked by sea.
+
+        Examples
+        --------
+
+        Mask a data variable by sea using the default method (regionmask):
+
+        >>> ds_masked = ds.spatial.mask_sea("tas")
+
+        Mask a data variable by sea using the PCMDI method with custom threshold:
+
+        >>> ds_masked = ds.spatial.mask_sea("tas", method="pcmdi", threshold=0.7)
+
+        Mask a data variable by sea using a custom mask and output the mask:
+
+        >>> custom_mask = xr.DataArray(...)  # Define your custom mask here
+        >>> ds_masked = ds.spatial.mask_sea("tas", mask=custom_mask, output_mask=True)
+
+        Mask a data variable by sea and add the mask to the dataset with a custom name:
+
+        >>> ds_masked = ds.spatial.mask_sea("tas", output_mask="sea_mask")
+        """
+        return generate_and_apply_land_sea_mask(
+            self._dataset,
+            data_var,
+            method,
+            keep="land",
+            threshold=threshold,
+            mask=mask,
+            output_mask=output_mask,
+            **options,
+        )
+
+    def generate_land_sea_mask(
+        self,
+        data_var: str | None = None,
+        method: str = "regionmask",
+        **options: Any,
+    ) -> xr.DataArray:
+        """
+        Generate a land-sea mask.
+
+        Parameters
+        ----------
+        data_var : str, optional
+            Name of the variable whose lat/lon coordinates will be used to
+            generate the land/sea mask. If omitted then a `mask` variable will
+            be generated using the lat/lon coordinates in the dataset.
+        method : str, optional
+            The method to use for generating the mask, by default "regionmask".
+            Supported methods: "regionmask", "pcmdi".
+        **options : Any
+            These options are passed directly to the ``method``. See specific
+            method documentation for available options:
+            :func:`xcdat.mask.pcmdi_land_sea_mask` for PCMDI options
+
+        Returns
+        -------
+        xr.DataArray
+            The land/sea mask.
+
+        Examples
+        --------
+
+        Generate a mask using the default method (regionmask):
+
+        >>> mask = ds.spatial.generate_land_sea_mask("tas")
+
+        Generate a mask using the "pcmdi" method:
+
+        >>> mask = ds.spatial.generate_land_sea_mask("tas", method="pcmdi")
+
+        Generate a mask using the "pcmdi" method, with customization:
+
+        >>> mask = ds.spatial.generate_land_sea_mask("tas", method="pcmdi", source=high_res_ds, source_data_var="highres")
+
+        Generating a mask from a new grid:
+
+        >>> grid = xc.create_uniform_grid(-90, 90, 1, 0, 359, 1)
+
+        >>> mask = grid.spatial.generate_land_sea_mask()
+        """
+        if data_var is None:
+            try:
+                da_shape = list(self._dataset.cf[x].shape[0] for x in ("X", "Y"))
+
+                da_dims = list(self._dataset.cf[x].name for x in ("X", "Y"))
+
+                da_coords = {x: self._dataset[x].copy() for x in da_dims}
+            except KeyError:
+                raise KeyError(
+                    "Dataset is missing a required coordinate, ensure a lat and lon coordinate exist"
+                ) from None
+
+            da = xr.DataArray(np.ones(da_shape), dims=da_dims, coords=da_coords)
+        else:
+            da = self._dataset[data_var]
+
+        return generate_land_sea_mask(da, method, **options)
+
     def average(
         self,
         data_var: str,
@@ -334,7 +545,7 @@ def _validate_axis_arg(self, axis: list[SpatialAxis] | tuple[SpatialAxis, ...]):
             get_dim_coords(self._dataset, key)
 
     def _validate_region_bounds(self, axis: SpatialAxis, bounds: RegionAxisBounds):
-        """Validates the ``bounds`` arg based on a set of criteria.
+        """Validates the ``bounds`` arg based on a set of threshold.
 
         Parameters
         ----------
@@ -666,7 +877,7 @@ def _combine_weights(self, axis_weights: AxisWeights) -> xr.DataArray:
     def _validate_weights(
         self, data_var: xr.DataArray, axis: list[SpatialAxis] | tuple[SpatialAxis, ...]
     ):
-        """Validates the ``weights`` arg based on a set of criteria.
+        """Validates the ``weights`` arg based on a set of threshold.
 
         This methods checks for the dimensional alignment between the
         ``weights`` and ``data_var``. It assumes that ``data_var`` has the same
@@ -723,7 +934,7 @@ def _averager(
         """Perform a weighted average of a data variable.
 
         This method assumes all specified keys in ``axis`` exists in the data
-        variable. Validation for this criteria is performed in
+        variable. Validation for this threshold is performed in
         ``_validate_weights()``.
 
         Operations include: