What happened?

I am trying to combine two datasets with partially overlapping data variables and exactly identical coordinates, using the compat="override" option. In the resulting dataset, the data variable, which appears in both datasets contains the values, from the dataset, placed second in the list of datasets to combine. The result is the same, if I switch the order of the two datasets in the call to combine_by_coords

What did you expect to happen?

I would expect, that for the data variables, which appear in both datasets, the values from the first dataset in the list, passed to combine_by_coords are used.

Minimal Complete Verifiable Example

```Python import numpy as np import pandas as pd import xarray as xr

temperature = np.random.randint(1,255,size=(9,10,10)) precipitation = np.random.randint(1,255,size=(9,10,10)) precipitation_alt = np.random.randint(255,1000,size=(9,10,10)) lon = np.linspace(10,100,10) lat = np.linspace(10,100,10) time_0 = pd.date_range("2014-09-06", periods=9, freq='2D')

ds_0 = xr.Dataset( data_vars=dict( temperature=(('time', 'y', 'x'), temperature), precipitation=(('time', 'y', 'x'), precipitation)), coords=dict( x=lon, y=lat, time=time_0) ) ds_1 = xr.Dataset( data_vars=dict( temperature=(('time', 'y', 'x'), temperature), precipitation=(('time', 'y', 'x'), precipitation_alt), precipitation2=(('time', 'y', 'x'), precipitation)), coords=dict( x=lon, y=lat, time=time_0)

)

res1 = xr.combine_by_coords([ds_0, ds_1], compat="override") res2 = xr.combine_by_coords([ds_1, ds_0], compat="override")

In the first case the resulting dataset should contain the same values in the variables precipitation and precipitation2, but it does not.

```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.
• [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

No response

Anything else we need to know?

No response

Environment

NSTALLED VERSIONS ------------------ commit: None python: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] python-bits: 64 OS: Linux OS-release: 5.15.0-91-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.9.3-development xarray: 2023.1.0 pandas: 2.0.3 numpy: 1.24.4 scipy: 1.10.1 netCDF4: 1.6.5 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.6.3 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.5 dask: None distributed: None matplotlib: 3.7.2 cartopy: None seaborn: None numbagg: None fsspec: None cupy: None pint: 0.21.1 sparse: None flox: None numpy_groupies: None setuptools: 68.2.2 pip: 23.3.1 conda: None pytest: None mypy: None IPython: 8.12.3 sphinx: None