diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index d0a1686..2b43c56 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -24,7 +24,7 @@ jobs:
         uses: conda-incubator/setup-miniconda@v2
         with:
           python-version: ${{ matrix.python-version }}
-          mamba-version: "*"
+          miniforge-variant: Mambaforge
           channels: conda-forge,defaults
           channel-priority: true
           auto-activate-base: false
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 284a0ae..cb23a2b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,12 +9,12 @@ repos:
       - id: double-quote-string-fixer
 
   - repo: https://github.com/ambv/black
-    rev: 20.8b1
+    rev: 22.3.0  # https://github.com/psf/black/issues/2964
     hooks:
       - id: black
         args: ["--line-length", "100", "--skip-string-normalization"]
 
-  - repo: https://gitlab.com/PyCQA/flake8
+  - repo: https://github.com/pycqa/flake8
     rev: 3.8.4
     hooks:
       - id: flake8
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 45f714a..351eed3 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -1,6 +1,11 @@
 # Required
 version: 2
 
+build:
+  os: "ubuntu-20.04"
+  tools:
+    python: "mambaforge-4.10"
+
 # Build documentation in the doc/ directory with Sphinx
 sphinx:
   configuration: doc/conf.py
@@ -9,7 +14,6 @@ conda:
     environment: environment_doc.yml
 
 python:
-  version: 3.8
   install:
     - method: pip
       path: .
diff --git a/doc/examples/introduction.ipynb b/doc/examples/introduction.ipynb
index 082a960..6a1b82f 100644
--- a/doc/examples/introduction.ipynb
+++ b/doc/examples/introduction.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,9 +44,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (x: 100, y: 100)\n",
+       "Coordinates:\n",
+       "    lat      (x, y) float64 -46.35 76.28 -85.01 -13.43 ... 62.3 -88.36 5.12\n",
+       "    lon      (x, y) float64 -1.377 53.78 52.71 -174.7 ... -164.6 -116.2 -51.76\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    field    (x, y) float64 -47.72 130.1 -32.3 -188.1 ... -102.3 -204.5 -46.64</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (x: 100, y: 100)\n",
+       "Coordinates:\n",
+       "    lat      (x, y) float64 -46.35 76.28 -85.01 -13.43 ... 62.3 -88.36 5.12\n",
+       "    lon      (x, y) float64 -1.377 53.78 52.71 -174.7 ... -164.6 -116.2 -51.76\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    field    (x, y) float64 -47.72 130.1 -32.3 -188.1 ... -102.3 -204.5 -46.64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds_mesh = xr.Dataset(\n",
     "    coords={'lat': (('x', 'y'), lat), 'lon': (('x', 'y'), lon)},\n",
@@ -67,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,9 +111,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (trajectory: 30)\n",
+       "Dimensions without coordinates: trajectory\n",
+       "Data variables:\n",
+       "    latitude   (trajectory) float64 -10.0 -8.276 -6.552 ... 36.55 38.28 40.0\n",
+       "    longitude  (trajectory) float64 -150.0 -139.7 -129.3 ... 129.3 139.7 150.0</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (trajectory: 30)\n",
+       "Dimensions without coordinates: trajectory\n",
+       "Data variables:\n",
+       "    latitude   (trajectory) float64 -10.0 -8.276 -6.552 ... 36.55 38.28 40.0\n",
+       "    longitude  (trajectory) float64 -150.0 -139.7 -129.3 ... 129.3 139.7 150.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds_trajectory = xr.Dataset({\n",
     "    'latitude': ('trajectory', np.linspace(-10, 40, 30)),\n",
@@ -106,9 +158,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (trajectory: 30)\n",
+       "Coordinates:\n",
+       "    lat      (trajectory) float64 -11.46 -8.57 -6.349 ... 37.13 38.05 41.56\n",
+       "    lon      (trajectory) float64 -150.6 -139.7 -129.9 ... 129.0 140.2 149.2\n",
+       "Dimensions without coordinates: trajectory\n",
+       "Data variables:\n",
+       "    field    (trajectory) float64 -162.1 -148.3 -136.3 ... 166.2 178.2 190.8</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (trajectory: 30)\n",
+       "Coordinates:\n",
+       "    lat      (trajectory) float64 -11.46 -8.57 -6.349 ... 37.13 38.05 41.56\n",
+       "    lon      (trajectory) float64 -150.6 -139.7 -129.9 ... 129.0 140.2 149.2\n",
+       "Dimensions without coordinates: trajectory\n",
+       "Data variables:\n",
+       "    field    (trajectory) float64 -162.1 -148.3 -136.3 ... 166.2 178.2 190.8"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds_selection = ds_mesh.xoak.sel(\n",
     "    lat=ds_trajectory.latitude,\n",
@@ -127,14 +207,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ds_trajectory.plot.scatter(x='longitude', y='latitude', c='k', alpha=0.7);\n",
     "ds_selection.plot.scatter(x='lon', y='lat', hue='field', alpha=0.9);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also return the distances associated with each selected point by inputting the name of the distance variable you would like used as the variable named in the returned Dataset. Note that if a DataArray was originally input, a Dataset will still be returned in this case. Distances are returned in kilometers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:   (trajectory: 30)\n",
+       "Coordinates:\n",
+       "    lat       (trajectory) float64 -11.46 -8.57 -6.349 ... 37.13 38.05 41.56\n",
+       "    lon       (trajectory) float64 -150.6 -139.7 -129.9 ... 129.0 140.2 149.2\n",
+       "Dimensions without coordinates: trajectory\n",
+       "Data variables:\n",
+       "    field     (trajectory) float64 -162.1 -148.3 -136.3 ... 166.2 178.2 190.8\n",
+       "    distance  (trajectory) float64 176.9 33.55 71.99 198.1 ... 69.07 52.98 185.2</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:   (trajectory: 30)\n",
+       "Coordinates:\n",
+       "    lat       (trajectory) float64 -11.46 -8.57 -6.349 ... 37.13 38.05 41.56\n",
+       "    lon       (trajectory) float64 -150.6 -139.7 -129.9 ... 129.0 140.2 149.2\n",
+       "Dimensions without coordinates: trajectory\n",
+       "Data variables:\n",
+       "    field     (trajectory) float64 -162.1 -148.3 -136.3 ... 166.2 178.2 190.8\n",
+       "    distance  (trajectory) float64 176.9 33.55 71.99 198.1 ... 69.07 52.98 185.2"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_selection = ds_mesh.xoak.sel(\n",
+    "    lat=ds_trajectory.latitude,\n",
+    "    lon=ds_trajectory.longitude,\n",
+    "    distances_name=\"distance\",\n",
+    ")\n",
+    "\n",
+    "ds_selection"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -144,9 +289,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (x: 10, y: 10)\n",
+       "Coordinates:\n",
+       "    lat      (x, y) float64 -26.81 52.21 54.89 81.46 ... -14.37 -6.231 56.52\n",
+       "    lon      (x, y) float64 -7.988 96.58 57.14 -122.8 ... 47.32 -177.5 -79.93\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    field    (x, y) float64 -34.79 148.8 112.0 -41.3 ... 32.96 -183.7 -23.41</pre>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (x: 10, y: 10)\n",
+       "Coordinates:\n",
+       "    lat      (x, y) float64 -26.81 52.21 54.89 81.46 ... -14.37 -6.231 56.52\n",
+       "    lon      (x, y) float64 -7.988 96.58 57.14 -122.8 ... 47.32 -177.5 -79.93\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    field    (x, y) float64 -34.79 148.8 112.0 -41.3 ... 32.96 -183.7 -23.41"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds_mesh2 = xr.Dataset({\n",
     "    'latitude': (('x', 'y'), np.random.uniform(-90, 90, size=(10, 10))),\n",
@@ -163,27 +336,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ds_mesh2.plot.scatter(x='longitude', y='latitude', c='k', alpha=0.7);\n",
     "ds_selection.plot.scatter(x='lon', y='lat', hue='field', alpha=0.9);"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:xoak_dev]",
+   "display_name": "Python 3.9.13 ('extract_model')",
    "language": "python",
-   "name": "conda-env-xoak_dev-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -195,7 +374,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8838a114cb9af02008a6426a5905618667030dfe4375f4e8b79cae9729b737db"
+   }
   }
  },
  "nbformat": 4,
diff --git a/src/xoak/accessor.py b/src/xoak/accessor.py
index 12e52f4..919f757 100644
--- a/src/xoak/accessor.py
+++ b/src/xoak/accessor.py
@@ -1,4 +1,4 @@
-from typing import Any, Hashable, Iterable, List, Mapping, Tuple, Type, Union
+from typing import Any, Hashable, Iterable, List, Mapping, Optional, Tuple, Type, Union
 
 import numpy as np
 import xarray as xr
@@ -134,12 +134,14 @@ def index(self) -> Union[None, Index, Iterable[Index]]:
             return [wrp.index for wrp in index_wrappers]
 
     def _query(self, indexers):
+        """Find the distance(s) and indices of nearest point(s)."""
         X = coords_to_point_array([indexers[c] for c in self._index_coords])
 
         if isinstance(X, np.ndarray) and isinstance(self._index, XoakIndexWrapper):
             # directly call index wrapper's query method
             res = self._index.query(X)
             results = res['indices'][:, 0]
+            distances = res['distances'][:, 0]
 
         else:
             # Two-stage lazy query with dask
@@ -195,7 +197,7 @@ def _query(self, indexers):
                 concatenate=True,
             )
 
-        return results
+        return results, distances
 
     def _get_pos_indexers(self, indices, indexers):
         """Returns positional indexers based on the query results and the
@@ -225,7 +227,10 @@ def _get_pos_indexers(self, indices, indexers):
         return pos_indexers
 
     def sel(
-        self, indexers: Mapping[Hashable, Any] = None, **indexers_kwargs: Any
+        self,
+        indexers: Mapping[Hashable, Any] = None,
+        distances_name: Optional[str] = None,
+        **indexers_kwargs: Any,
     ) -> Union[xr.Dataset, xr.DataArray]:
         """Selection based on a ball tree index.
 
@@ -243,6 +248,18 @@ def sel(
         This triggers :func:`dask.compute` if the given indexers and/or the index
         coordinates are chunked.
 
+        Parameters
+        ----------
+        distances_name: str, optional
+            If a string is input, it is used to save the distances into the xarray
+            object. Distances are in km.
+
+        Returns
+        -------
+        xr.Dataset, xr.DataArray
+            Normally, the type input is the type output. However, if you input a
+            str for `distances_name`, the return type will be Dataset to
+            accommodate the additional variable.
         """
         if not getattr(self, '_index', False):
             raise ValueError(
@@ -250,7 +267,7 @@ def sel(
             )
 
         indexers = either_dict_or_kwargs(indexers, indexers_kwargs, 'xoak.sel')
-        indices = self._query(indexers)
+        indices, distances = self._query(indexers)
 
         if not isinstance(indices, np.ndarray):
             # TODO: remove (see todo below)
@@ -263,4 +280,23 @@ def sel(
         # This would also allow lazy selection
         result = self._xarray_obj.isel(indexers=pos_indexers)
 
+        # save distances as a new variable in xarray object if name is input
+        if distances_name is not None or (
+            isinstance(distances_name, str) and len(distances_name) == 0
+        ):
+            # need to have a Dataset instead of DataArray to add a new variable
+            # otherwise goes in as a coordinate
+            if not isinstance(result, xr.Dataset):
+                result = result.to_dataset()
+            # use same dimensions as indexers
+            attrs = {
+                'long_name': 'Distance from location to nearest comparison point.',
+            }
+
+            indexer_dim = list(indexers.values())[0].dims
+            indexer_shape = indexers[list(indexers.keys())[0]].shape
+            result[distances_name] = xr.Variable(
+                indexer_dim, distances.reshape(indexer_shape), attrs
+            )
+
         return result
diff --git a/src/xoak/tests/conftest.py b/src/xoak/tests/conftest.py
index e8b4314..eec5782 100644
--- a/src/xoak/tests/conftest.py
+++ b/src/xoak/tests/conftest.py
@@ -1,4 +1,5 @@
 import dask
+import dask.array
 import numpy as np
 import pytest
 import xarray as xr
diff --git a/src/xoak/tests/test_accessor.py b/src/xoak/tests/test_accessor.py
index 32877ac..a35aae3 100644
--- a/src/xoak/tests/test_accessor.py
+++ b/src/xoak/tests/test_accessor.py
@@ -1,3 +1,4 @@
+import numpy as np
 import pytest
 import xarray as xr
 from scipy.spatial import cKDTree
@@ -80,3 +81,24 @@ def test_index_property():
     ds_chunk = ds.chunk(2)
     ds_chunk.xoak.set_index(['x', 'y'], 'scipy_kdtree')
     assert isinstance(ds_chunk.xoak.index, list)
+
+
+def test_distances():
+
+    ds = xr.Dataset(
+        coords={
+            'x': ('a', [0, 1, 2, 3]),
+            'y': ('a', [0, 1, 2, 3]),
+        }
+    )
+
+    ds_to_find = xr.Dataset({'lat_to_find': ('a', [0, 0]), 'lon_to_find': ('a', [0, 0.5])})
+    ds.xoak.set_index(['y', 'x'], 'sklearn_geo_balltree')
+
+    output = ds.xoak.sel(
+        {'y': ds_to_find.lat_to_find, 'x': ds_to_find.lon_to_find}, distances_name='distances'
+    )
+
+    assert isinstance(output, xr.Dataset)
+    # this distance is in radians
+    np.testing.assert_allclose(output['distances'], [0, 0.008726646259971648])