MCVE Code Sample

```python import xarray as xr import numpy as np

d1 = xr.DataArray(np.arange(4), dims=["a"], coords=dict(a=[0, 1, 2, 3])) d2 = xr.DataArray(np.arange(4), dims=["a"], coords=dict(a=[0, 1, 2, 3]))

note: different coords

d3 = xr.DataArray(np.arange(4), dims=["a"], coords=dict(a=[1, 2, 3, 4]))

(d1 * d2).sum() # -> array(14) xr.dot(d1, d2) # -> array(14)

(d2 * d3).sum() # -> array(8) xr.dot(d2, d3) # -> ValueError

```

Expected Output

python <xarray.DataArray ()> array(8)

Problem Description

The last statement results in an python ValueError: indexes along dimension 'a' are not equal because xr.apply_ufunc defaults to join='exact'. However, I think this should work - but maybe there is a good reason for this to fail?

This is a problem for #2922 (weighted operations) - I think it is fine for the weights and data to not align.

Fixing this may be as easy as specifying join='inner' in

https://github.com/pydata/xarray/blob/e0fd48052dbda34ee35d2491e4fe856495c9621b/xarray/core/computation.py#L1181-L1187

@fujiisoup

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: 5afc6f32b18f5dbb9a89e30f156b626b0a83597d python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.36-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.6.2 xarray: 0.14.0+164.g5afc6f32.dirty pandas: 0.25.2 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.1.2 pydap: installed h5netcdf: 0.7.4 h5py: 2.10.0 Nio: 1.5.5 zarr: 2.3.2 cftime: 1.0.4.2 nc_time_axis: 1.2.0 PseudoNetCDF: installed rasterio: 1.1.0 cfgrib: 0.9.7.2 iris: 2.2.0 bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: installed setuptools: 41.6.0.post20191029 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: None