Is your feature request related to a problem?

I want to do coord1.isin.coord2 and it is quite slow when coords are large and of object type CFTimeIndex.

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

n=1000 coord1 = xr.cftime_range(start='2000', freq='MS', periods=n) coord2 = xr.cftime_range(start='2000', freq='3MS', periods=n)

cftimeindex: very fast

%timeit coord1.isin(coord2) # 743 µs ± 1.33 µs

np.isin on index.asi8

%timeit np.isin(coord1.asi8,coord2.asi8) # 7.83 ms ± 14.1 µs

da = xr.DataArray(np.random.random((n,n)),dims=['a','b'],coords={'a':coord1,'b':coord2})

when xr.DataArray coordinate slow

%timeit da.a.isin(da.b) # 94.9 ms ± 959 µs

when converting xr.DataArray coordinate back to index slow

%timeit np.isin(da.a.to_index(), da.b.to_index()) # 97.4 ms ± 819 µs

when converting xr.DataArray coordinate back to index asi

%timeit np.isin(da.a.to_index().asi8, da.b.to_index().asi8) # 7.89 ms ± 15.2 µs ```

Describe the solution you'd like

faster coord1.isin.coord2 by default. could we re-route here, e.g. to the alternative?

conversion from coordinate to_index() is costly I guess

Describe alternatives you've considered

np.isin(coord1.to_index().asi8, coord2.to_index().asi8 brings me nice speedups in https://github.com/pangeo-data/climpred/pull/724

Additional context

unsure whether this issue should go here on in cftime