Repository: pangeo-data/xESMF

Issue:
Regridding drops coordinates
When regridding, any coordinate other than `lon` and `lat` are lost.

MWE:
```python3
import numpy as np
import xarray as xr
import xesmf as xe

ds_out = xr.tutorial.open_dataset("rasm")
# Create coords for the curvilinear dimensions
ds_out['x'] = np.arange(ds_out.x.size)
ds_out['y'] = np.arange(ds_out.y.size)

ds_in = xr.tutorial.open_dataset('air_temperature')

reg = xe.Regridder(ds_in, ds_out, 'bilinear')
out = reg(ds_in)
print(out)
```
Where you see: `Dimensions without coordinates: y, x`. However, these coordinates exist on `ds_out`, they just were not passed to the output. 

This could be solved by saving the coordinates or `ds_out` that share dimensions with `lon` and `lat` and then assigning them back after the regridding. Should be simple, no? I might have time to do something.

Issue first raised by @RondeauG .

EDIT : Changed the title as this applies for any coordinates, it's just that with curvilinear grids we often need those 1D coordinates for the data to make sense. (Not the case here with 'rasm',  but happens all the time with rotated pole data).

Discussion:
review-notebook-app[bot]: Check out this pull request on&nbsp; <a href="https://app.reviewnb.com/pangeo-data/xESMF/pull/175"><img align="absmiddle"  alt="ReviewNB" height="28" class="BotMessageButtonImage" src="https://raw.githubusercontent.com/ReviewNB/support/master/images/button_reviewnb.png"/></a> 

 See visual diffs & provide feedback on Jupyter Notebooks. 

---

 <i>Powered by <a href='https://www.reviewnb.com/?utm_source=gh'>ReviewNB</a></i>
raphaeldussin: great job @aulemahal 
LGTM as well

Patch:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ebb2dfd..8f1a309 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.2.0
+    rev: v4.3.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
@@ -10,7 +10,7 @@ repos:
       - id: double-quote-string-fixer
 
   - repo: https://github.com/psf/black
-    rev: 22.3.0
+    rev: 22.6.0
     hooks:
       - id: black
 
@@ -34,7 +34,7 @@ repos:
       - id: isort
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v2.6.2
+    rev: v2.7.1
     hooks:
       - id: prettier
 
diff --git a/CHANGES.rst b/CHANGES.rst
index 73d0f71..2289eef 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -4,10 +4,16 @@ What's new
 0.6.3 (unreleased)
 ------------------
 
+Bug fixes
+~~~~~~~~~
+- Spatial coordinates of `ds_out` are kept within the regridder and transferred to the regridded DataArray or Dataset (:pull:`175`). By `Pascal Bourgault <https://github.com/aulemahal>`_
+- Added `numba` as an explicit dependency to fix installation with conda (:pull:`168`). By `Pascal Bourgault <https://github.com/aulemahal>`_
+
 Internal changes
 ~~~~~~~~~~~~~~~~
 - Use `cf-xarray` to guess missing CF coordinates before extracting bounds (:pull:`147`). By `Pascal Bourgault <https://github.com/aulemahal>`_
 
+
 0.6.2 (23-11-2021)
 ------------------
 
diff --git a/doc/notebooks/Compare_algorithms.ipynb b/doc/notebooks/Compare_algorithms.ipynb
index 8ad131a..9697120 100644
--- a/doc/notebooks/Compare_algorithms.ipynb
+++ b/doc/notebooks/Compare_algorithms.ipynb
@@ -94,13 +94,11 @@
        "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<title>Show/Hide data repr</title>\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
        "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
        "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
        "</symbol>\n",
        "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<title>Show/Hide attributes</title>\n",
        "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
        "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
        "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
@@ -123,11 +121,29 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
        ".xr-wrap {\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
        "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
@@ -390,7 +406,8 @@
        "  grid-template-columns: 125px auto;\n",
        "}\n",
        "\n",
-       ".xr-attrs dt, dd {\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
        "  padding: 0;\n",
        "  margin: 0;\n",
        "  float: left;\n",
@@ -425,7 +442,16 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><div class='xr-wrap'><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-38dc4e53-ac73-4b2e-8340-caf46fd32a62' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-38dc4e53-ac73-4b2e-8340-caf46fd32a62' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 18</li><li><span>x_b</span>: 19</li><li><span>y</span>: 12</li><li><span>y_b</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad5ecefd-1f6b-4c8f-a020-d60ebc131f1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad5ecefd-1f6b-4c8f-a020-d60ebc131f1b' 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>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-170.0 -150.0 ... 150.0 170.0</div><input id='attrs-0f6e926a-7bfd-430d-8c6c-12b2e27d5eb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f6e926a-7bfd-430d-8c6c-12b2e27d5eb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173690be-b5b6-49f4-89e8-ee6c6c60df49' class='xr-var-data-in' type='checkbox'><label for='data-173690be-b5b6-49f4-89e8-ee6c6c60df49' 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'></dl></div><pre class='xr-var-data'>array([[-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (y: 12, x: 18, y_b: 13, x_b: 19)\n",
+       "Coordinates:\n",
+       "    lon      (y, x) float64 -170.0 -150.0 -130.0 -110.0 ... 130.0 150.0 170.0\n",
+       "    lat      (y, x) float64 -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\n",
+       "    lon_b    (y_b, x_b) int64 -180 -160 -140 -120 -100 ... 100 120 140 160 180\n",
+       "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\n",
+       "Dimensions without coordinates: y, x, y_b, x_b\n",
+       "Data variables:\n",
+       "    data     (y, x) float64 2.016 2.009 1.997 1.987 ... 1.987 1.997 2.009 2.016</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-187c0206-7767-449a-bcd6-4578dc14f029' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-187c0206-7767-449a-bcd6-4578dc14f029' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 12</li><li><span>x</span>: 18</li><li><span>y_b</span>: 13</li><li><span>x_b</span>: 19</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6e5c386d-cb88-40dc-b4e1-1642752d466c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e5c386d-cb88-40dc-b4e1-1642752d466c' 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>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-170.0 -150.0 ... 150.0 170.0</div><input id='attrs-d9e463e1-eca8-452f-bfc3-f00396173aa0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9e463e1-eca8-452f-bfc3-f00396173aa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c595e8f-8f35-42e3-a1e3-3d2311e263c4' class='xr-var-data-in' type='checkbox'><label for='data-3c595e8f-8f35-42e3-a1e3-3d2311e263c4' 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>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
        "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
        "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
        "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
@@ -448,7 +474,7 @@
        "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
        "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
        "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
-       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-82.5 -82.5 -82.5 ... 82.5 82.5</div><input id='attrs-454cf934-37b5-4dfe-855c-2bd3a42ef38e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-454cf934-37b5-4dfe-855c-2bd3a42ef38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de2cf556-82d2-40c2-a3d2-0e675913b3da' class='xr-var-data-in' type='checkbox'><label for='data-de2cf556-82d2-40c2-a3d2-0e675913b3da' 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'></dl></div><pre class='xr-var-data'>array([[-82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5,\n",
+       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-82.5 -82.5 -82.5 ... 82.5 82.5</div><input id='attrs-18accfcf-19e1-4b5f-9d0e-5bcc35659766' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18accfcf-19e1-4b5f-9d0e-5bcc35659766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c7020b9-525b-4eb2-adab-307ebd08c4b8' class='xr-var-data-in' type='checkbox'><label for='data-0c7020b9-525b-4eb2-adab-307ebd08c4b8' 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>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[-82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5,\n",
        "        -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5],\n",
        "       [-67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5,\n",
        "        -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5],\n",
@@ -471,7 +497,7 @@
        "       [ 67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,\n",
        "         67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5],\n",
        "       [ 82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,\n",
-       "         82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-180 -160 -140 -120 ... 140 160 180</div><input id='attrs-b18f5c3b-0539-4fef-9969-3e5091e320e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b18f5c3b-0539-4fef-9969-3e5091e320e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02f906c8-e7aa-4844-a41a-28d9370b9224' class='xr-var-data-in' type='checkbox'><label for='data-02f906c8-e7aa-4844-a41a-28d9370b9224' 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'></dl></div><pre class='xr-var-data'>array([[-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
+       "         82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-180 -160 -140 -120 ... 140 160 180</div><input id='attrs-87d0ed7c-ab03-4c0f-b0ca-0d553c512e32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87d0ed7c-ab03-4c0f-b0ca-0d553c512e32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5d976d4-36b7-401d-8f7a-25f77ed3e6c7' class='xr-var-data-in' type='checkbox'><label for='data-a5d976d4-36b7-401d-8f7a-25f77ed3e6c7' 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'></dl></div><div class='xr-var-data'><pre>array([[-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
        "          40,   60,   80,  100,  120,  140,  160,  180],\n",
        "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
        "          40,   60,   80,  100,  120,  140,  160,  180],\n",
@@ -496,7 +522,7 @@
        "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
        "          40,   60,   80,  100,  120,  140,  160,  180],\n",
        "       [-180, -160, -140, -120, -100,  -80,  -60,  -40,  -20,    0,   20,\n",
-       "          40,   60,   80,  100,  120,  140,  160,  180]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-90 -90 -90 -90 -90 ... 90 90 90 90</div><input id='attrs-0ee818cd-9bd9-4e97-8cf2-05f582a5a063' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ee818cd-9bd9-4e97-8cf2-05f582a5a063' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b29f280-e697-423e-9f5c-e3af379cbc50' class='xr-var-data-in' type='checkbox'><label for='data-6b29f280-e697-423e-9f5c-e3af379cbc50' 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'></dl></div><pre class='xr-var-data'>array([[-90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90,\n",
+       "          40,   60,   80,  100,  120,  140,  160,  180]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-90 -90 -90 -90 -90 ... 90 90 90 90</div><input id='attrs-11ce0451-8c5c-438c-9d90-536abb48ba9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11ce0451-8c5c-438c-9d90-536abb48ba9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6ff652c-5f1a-4629-9d05-f9ce953c92ef' class='xr-var-data-in' type='checkbox'><label for='data-e6ff652c-5f1a-4629-9d05-f9ce953c92ef' 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'></dl></div><div class='xr-var-data'><pre>array([[-90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90, -90,\n",
        "        -90, -90, -90, -90, -90, -90],\n",
        "       [-75, -75, -75, -75, -75, -75, -75, -75, -75, -75, -75, -75, -75,\n",
        "        -75, -75, -75, -75, -75, -75],\n",
@@ -521,7 +547,7 @@
        "       [ 75,  75,  75,  75,  75,  75,  75,  75,  75,  75,  75,  75,  75,\n",
        "         75,  75,  75,  75,  75,  75],\n",
        "       [ 90,  90,  90,  90,  90,  90,  90,  90,  90,  90,  90,  90,  90,\n",
-       "         90,  90,  90,  90,  90,  90]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-eeaa86c3-130e-4823-8fec-b8cb83bddb89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eeaa86c3-130e-4823-8fec-b8cb83bddb89' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.016 2.009 1.997 ... 2.009 2.016</div><input id='attrs-e94d21a2-2aea-49e6-bcfb-5f8ee7a1c4f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e94d21a2-2aea-49e6-bcfb-5f8ee7a1c4f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daddae96-87bd-4623-97de-22eb4ac30c04' class='xr-var-data-in' type='checkbox'><label for='data-daddae96-87bd-4623-97de-22eb4ac30c04' 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'></dl></div><pre class='xr-var-data'>array([[2.01600962, 2.00851854, 1.99704154, 1.98694883, 1.98296291,\n",
+       "         90,  90,  90,  90,  90,  90]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-482ec81f-600a-441c-a1aa-1a88b0daa00b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-482ec81f-600a-441c-a1aa-1a88b0daa00b' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.016 2.009 1.997 ... 2.009 2.016</div><input id='attrs-343ed3cf-e94d-4159-9697-507ca685d552' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-343ed3cf-e94d-4159-9697-507ca685d552' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36c0f8f8-0e99-4604-86ca-3d649d29b7f8' class='xr-var-data-in' type='checkbox'><label for='data-36c0f8f8-0e99-4604-86ca-3d649d29b7f8' 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'></dl></div><div class='xr-var-data'><pre>array([[2.01600962, 2.00851854, 1.99704154, 1.98694883, 1.98296291,\n",
        "        1.98694883, 1.99704154, 2.00851854, 2.01600962, 2.01600962,\n",
        "        2.00851854, 1.99704154, 1.98694883, 1.98296291, 1.98694883,\n",
        "        1.99704154, 2.00851854, 2.01600962],\n",
@@ -541,14 +567,7 @@
        "        1.34614017, 1.85178201, 2.4267767 , 2.80207782, 2.80207782,\n",
        "        2.4267767 , 1.85178201, 1.34614017, 1.14644661, 1.34614017,\n",
        "        1.85178201, 2.4267767 , 2.80207782],\n",
-       "       [2.923683  , 2.49148146, 1.82931028, 1.24700672, 1.01703709,\n",
-       "        1.24700672, 1.82931028, 2.49148146, 2.923683  , 2.923683  ,\n",
-       "        2.49148146, 1.82931028, 1.24700672, 1.01703709, 1.24700672,\n",
-       "        1.82931028, 2.49148146, 2.923683  ],\n",
-       "       [2.923683  , 2.49148146, 1.82931028, 1.24700672, 1.01703709,\n",
-       "        1.24700672, 1.82931028, 2.49148146, 2.923683  , 2.923683  ,\n",
-       "        2.49148146, 1.82931028, 1.24700672, 1.01703709, 1.24700672,\n",
-       "        1.82931028, 2.49148146, 2.923683  ],\n",
+       "...\n",
        "       [2.80207782, 2.4267767 , 1.85178201, 1.34614017, 1.14644661,\n",
        "        1.34614017, 1.85178201, 2.4267767 , 2.80207782, 2.80207782,\n",
        "        2.4267767 , 1.85178201, 1.34614017, 1.14644661, 1.34614017,\n",
@@ -568,17 +587,17 @@
        "       [2.01600962, 2.00851854, 1.99704154, 1.98694883, 1.98296291,\n",
        "        1.98694883, 1.99704154, 2.00851854, 2.01600962, 2.01600962,\n",
        "        2.00851854, 1.99704154, 1.98694883, 1.98296291, 1.98694883,\n",
-       "        1.99704154, 2.00851854, 2.01600962]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-89896086-879d-4259-9564-4d138efb14d0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89896086-879d-4259-9564-4d138efb14d0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "        1.99704154, 2.00851854, 2.01600962]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26552692-6f21-4ce3-a7e0-83a71218e7e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26552692-6f21-4ce3-a7e0-83a71218e7e9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:  (x: 18, x_b: 19, y: 12, y_b: 13)\n",
+       "Dimensions:  (y: 12, x: 18, y_b: 13, x_b: 19)\n",
        "Coordinates:\n",
        "    lon      (y, x) float64 -170.0 -150.0 -130.0 -110.0 ... 130.0 150.0 170.0\n",
        "    lat      (y, x) float64 -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\n",
        "    lon_b    (y_b, x_b) int64 -180 -160 -140 -120 -100 ... 100 120 140 160 180\n",
        "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\n",
-       "Dimensions without coordinates: x, x_b, y, y_b\n",
+       "Dimensions without coordinates: y, x, y_b, x_b\n",
        "Data variables:\n",
        "    data     (y, x) float64 2.016 2.009 1.997 1.987 ... 1.987 1.997 2.009 2.016"
       ]
@@ -601,7 +620,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f61b2da6400>"
+       "<matplotlib.collections.QuadMesh at 0x7f0de6ed2040>"
       ]
      },
      "execution_count": 5,
@@ -610,7 +629,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -662,20 +681,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 69.9 ms, sys: 12.4 ms, total: 82.3 ms\n",
-      "Wall time: 98.5 ms\n",
+      "CPU times: user 414 ms, sys: 13.1 ms, total: 427 ms\n",
+      "Wall time: 444 ms\n",
       "\n",
-      "CPU times: user 109 ms, sys: 0 ns, total: 109 ms\n",
-      "Wall time: 108 ms\n",
+      "CPU times: user 82.3 ms, sys: 0 ns, total: 82.3 ms\n",
+      "Wall time: 82.3 ms\n",
       "\n",
-      "CPU times: user 30.3 ms, sys: 60 µs, total: 30.4 ms\n",
-      "Wall time: 30 ms\n",
+      "CPU times: user 24.2 ms, sys: 0 ns, total: 24.2 ms\n",
+      "Wall time: 24.3 ms\n",
       "\n",
-      "CPU times: user 10.1 ms, sys: 0 ns, total: 10.1 ms\n",
-      "Wall time: 9.9 ms\n",
+      "CPU times: user 8.21 ms, sys: 0 ns, total: 8.21 ms\n",
+      "Wall time: 8.23 ms\n",
       "\n",
-      "CPU times: user 467 ms, sys: 14.7 ms, total: 482 ms\n",
-      "Wall time: 481 ms\n",
+      "CPU times: user 476 ms, sys: 2.7 ms, total: 478 ms\n",
+      "Wall time: 478 ms\n",
       "\n"
      ]
     }
@@ -700,7 +719,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x576 with 11 Axes>"
       ]
@@ -718,7 +737,7 @@
     "    ax = axes.flatten()[i]\n",
     "    ds_fine[method].plot.pcolormesh(ax=ax)\n",
     "    ax.set_title(method, fontsize=15)\n",
-    "\n",
+    "axes[-1, -1].set_visible(False)\n",
     "plt.tight_layout()"
    ]
   },
@@ -762,7 +781,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x576 with 11 Axes>"
       ]
@@ -780,7 +799,7 @@
     "    ax = axes.flatten()[i]\n",
     "    ds_coarse[method].plot.pcolormesh(ax=ax)\n",
     "    ax.set_title(method, fontsize=15)\n",
-    "\n",
+    "axes[-1, -1].set_visible(False)\n",
     "plt.tight_layout()"
    ]
   },
@@ -805,7 +824,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -819,7 +838,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.9.13"
   },
   "toc": {
    "nav_menu": {},
diff --git a/xesmf/frontend.py b/xesmf/frontend.py
index f56de30..b5836c5 100644
--- a/xesmf/frontend.py
+++ b/xesmf/frontend.py
@@ -18,7 +18,7 @@ from .smm import (
     check_shapes,
     read_weights,
 )
-from .util import split_polygons_and_holes
+from .util import LAT_CF_ATTRS, LON_CF_ATTRS, split_polygons_and_holes
 
 try:
     import dask.array as da
@@ -783,59 +783,55 @@ class Regridder(BaseRegridder):
 
         # record output grid and metadata
         lon_out, lat_out = _get_lon_lat(ds_out)
-        self._lon_out, self._lat_out = np.asarray(lon_out), np.asarray(lat_out)
-        self._coord_names = dict(
-            lon=lon_out.name if isinstance(lon_out, DataArray) else 'lon',
-            lat=lat_out.name if isinstance(lat_out, DataArray) else 'lat',
-        )
-        self._lon_out_attrs = lon_out.attrs if isinstance(lon_out, DataArray) else {}
-        self._lat_out_attrs = lat_out.attrs if isinstance(lat_out, DataArray) else {}
-
-        if self._lon_out.ndim == 2:
-            try:
-                self.lon_dim = self.lat_dim = lon_out.dims
-            except Exception:
-                self.lon_dim = self.lat_dim = ('y', 'x')
-
-            self.out_horiz_dims = self.lon_dim
-
-        elif self._lon_out.ndim == 1:
-            try:
-                (self.lon_dim,) = lon_out.dims
-                (self.lat_dim,) = lat_out.dims
-            except Exception:
-                self.lon_dim = 'lon'
-                self.lat_dim = 'lat'
-
-            self.out_horiz_dims = (self.lat_dim, self.lon_dim)
+        if not isinstance(lon_out, DataArray):
+            if lon_out.ndim == 2:
+                dims = [('y', 'x'), ('y', 'x')]
+            elif self.sequence_out:
+                dims = [('locations',), ('locations',)]
+            else:
+                dims = [('lon',), ('lat',)]
+            lon_out = xr.DataArray(lon_out, dims=dims[0], name='lon', attrs=LON_CF_ATTRS)
+            lat_out = xr.DataArray(lat_out, dims=dims[1], name='lat', attrs=LAT_CF_ATTRS)
+
+        if lat_out.ndim == 2:
+            self.out_horiz_dims = lat_out.dims
+        elif self.sequence_out:
+            if lat_out.dims != lon_out.dims:
+                raise ValueError(
+                    'Regridder expects a locstream output, but the passed longitude '
+                    'and latitude are not specified along the same dimension. '
+                    f'(lon: {lon_out.dims}, lat: {lat_out.dims})'
+                )
+            self.out_horiz_dims = ('dummy',) + lat_out.dims
+        else:
+            self.out_horiz_dims = (lat_out.dims[0], lon_out.dims[0])
+
+        if isinstance(ds_out, Dataset):
+            self.out_coords = {
+                name: crd
+                for name, crd in ds_out.coords.items()
+                if set(self.out_horiz_dims).issuperset(crd.dims)
+            }
+            grid_mapping = {
+                var.attrs['grid_mapping']
+                for var in ds_out.data_vars.values()
+                if 'grid_mapping' in var.attrs
+            }
+            if grid_mapping:
+                self.out_coords.update({gm: ds_out[gm] for gm in grid_mapping if gm in ds_out})
+        else:
+            self.out_coords = {lat_out.name: lat_out, lon_out.name: lon_out}
 
     def _format_xroutput(self, out, new_dims=None):
-        if not self.sequence_out and new_dims is not None:
+        if new_dims is not None:
             # rename dimension name to match output grid
-            out = out.rename(
-                {new_dims[0]: self.out_horiz_dims[0], new_dims[1]: self.out_horiz_dims[1]}
-            )
-
-        # append output horizontal coordinate values
-        # extra coordinates are automatically tracked by apply_ufunc
-        lon_args = dict(data=self._lon_out, attrs=self._lon_out_attrs)
-        lat_args = dict(data=self._lat_out, attrs=self._lat_out_attrs)
-        if self.sequence_out:
-            out.coords['lon'] = xr.DataArray(**lon_args, dims=('locations',))
-            out.coords['lat'] = xr.DataArray(**lat_args, dims=('locations',))
-        else:
-            out.coords['lon'] = xr.DataArray(**lon_args, dims=self.lon_dim)
-            out.coords['lat'] = xr.DataArray(**lat_args, dims=self.lat_dim)
+            out = out.rename({nd: od for nd, od in zip(new_dims, self.out_horiz_dims)})
 
+        out = out.assign_coords(**self.out_coords)
         out.attrs['regrid_method'] = self.method
 
         if self.sequence_out:
             out = out.squeeze(dim='dummy')
-            if self.lon_dim == self.lat_dim:
-                out = out.rename(locations=self.lon_dim)
-
-        # Use ds_out coordinates
-        out = out.rename(self._coord_names)
 
         return out
 
diff --git a/xesmf/util.py b/xesmf/util.py
index f012eaf..ee379aa 100644
--- a/xesmf/util.py
+++ b/xesmf/util.py
@@ -4,6 +4,9 @@ import numpy as np
 import xarray as xr
 from shapely.geometry import MultiPolygon, Polygon
 
+LON_CF_ATTRS = {'standard_name': 'longitude', 'units': 'degrees_east'}
+LAT_CF_ATTRS = {'standard_name': 'latitude', 'units': 'degrees_north'}
+
 
 def _grid_1d(start_b, end_b, step):
     """
@@ -105,13 +108,14 @@ def cf_grid_2d(lon0_b, lon1_b, d_lon, lat0_b, lat1_b, d_lat):
             'lon': (
                 'lon',
                 lon_1d,
-                {'standard_name': 'longitude', 'units': 'degrees_east', 'bounds': 'lon_bounds'},
+                {'bounds': 'lon_bounds', **LON_CF_ATTRS},
             ),
             'lat': (
                 'lat',
                 lat_1d,
-                {'standard_name': 'latitude', 'units': 'degrees_north', 'bounds': 'lat_bounds'},
+                {'bounds': 'lat_bounds', **LAT_CF_ATTRS},
             ),
+            'latitude_longitude': xr.DataArray(),
         },
         data_vars={
             'lon_bounds': vertices_to_bounds(lon_b_1d, ('bound', 'lon')),


Test patch:
diff --git a/xesmf/tests/test_frontend.py b/xesmf/tests/test_frontend.py
index 47516e9..b8c9b0e 100644
--- a/xesmf/tests/test_frontend.py
+++ b/xesmf/tests/test_frontend.py
@@ -302,7 +302,7 @@ def test_regridded_respects_input_dtype(dtype, data_in):
     regridder = xe.Regridder(ds_in, ds_out, 'bilinear')  # Make this a fixture?
     out = regridder(data_in)
 
-    if 'data' in data_in:
+    if isinstance(data_in, xr.Dataset):
         # When data_in is xr.Dataset, a mapping...
         assert out['data'].dtype == data_in['data'].dtype
     else:
@@ -567,7 +567,6 @@ def test_regrid_dataarray_dask_from_locstream(request, scheduler):
 
 def test_regrid_dataset():
     # xarray.Dataset containing in-memory numpy array
-
     regridder = xe.Regridder(ds_in, ds_out, 'conservative')
 
     # `ds_out` already refers to output grid object
@@ -602,6 +601,25 @@ def test_regrid_dataset():
     xr.testing.assert_identical(ds_result2, ds_result)
 
 
+def test_regrid_dataset_extracoords():
+    ds_out2 = ds_out.copy().assign_coords(
+        x=np.arange(24),
+        y=np.arange(20),  # coords to be transfered
+        latitude_longitude=xr.DataArray(),  # grid_mapping
+        bogus=ds_out.lev * ds_out.lon,  # coord not to be transfered
+    )
+    ds_out2['data_ref'].attrs['grid_mapping'] = 'latitude_longitude'
+    ds_out2['data4D_ref'].attrs['grid_mapping'] = 'latitude_longitude'
+
+    regridder = xe.Regridder(ds_in, ds_out2, 'conservative')
+    ds_result = regridder(ds_in)
+
+    assert 'x' in ds_result.coords
+    assert 'y' in ds_result.coords
+    assert 'bogus' not in ds_result.coords
+    assert 'latitude_longitude' in ds_result.coords
+
+
 @pytest.mark.parametrize('scheduler', dask_schedulers)
 def test_regrid_dataset_dask(request, scheduler):
     scheduler = request.getfixturevalue(scheduler)
@@ -788,7 +806,7 @@ def test_non_cf_latlon():
     [
         ({}, 'locations'),
         ({'lon': {'locations': 'foo'}, 'lat': {'locations': 'foo'}}, 'foo'),
-        ({'lon': {'locations': 'foo'}, 'lat': {'locations': 'bar'}}, 'locations'),
+        ({'lon': {'locations': 'foo'}, 'lat': {'locations': 'bar'}}, None),
     ],
 )
 def test_locstream_dim_name(var_renamer, dim_out):
@@ -797,10 +815,14 @@ def test_locstream_dim_name(var_renamer, dim_out):
     for var, renamer in var_renamer.items():
         ds_locs_renamed[var] = ds_locs_renamed[var].rename(renamer)
 
-    regridder = xe.Regridder(ds_in, ds_locs_renamed, 'bilinear', locstream_out=True)
-    expected = {'lev', 'time', 'x_b', 'y_b', dim_out}
-    actual = set(regridder(ds_in).dims)
-    assert expected == actual
+    if dim_out is None:
+        with pytest.raises(ValueError, match='not specified along the same dimension'):
+            regridder = xe.Regridder(ds_in, ds_locs_renamed, 'bilinear', locstream_out=True)
+    else:
+        regridder = xe.Regridder(ds_in, ds_locs_renamed, 'bilinear', locstream_out=True)
+        expected = {'lev', 'time', 'x_b', 'y_b', dim_out}
+        actual = set(regridder(ds_in).dims)
+        assert expected == actual
 
 
 def test_spatial_averager_mask():