What happened?

There is still something wrong with the groupby indexing changes from 2023.04.0 onward. Here are two typical ways of doing a seasonal anomaly calculation, which return the same result on 2023.03.0 but are different on 2023.04.2:

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

monthly timeseries that should return "zero anomalies" everywhere

time = xr.date_range("2023-01-01", "2023-12-31", freq="MS") data = np.linspace(-1, 1, 12) x = xr.DataArray(data, coords={"time": time}) clim = xr.DataArray(data, coords={"month": np.arange(1, 13, 1)})

seems to give the correct result if we use the full x, but not with a slice

x_slice = x.sel(time=["2023-04-01"])

two typical ways of computing anomalies

anom_gb = x_slice.groupby("time.month") - clim anom_sel = x_slice - clim.sel(month=x_slice.time.dt.month)

passes on 2023.3.0, fails on 2023.4.2

the groupby version is aligning the indexes wrong, giving us something other than 0

assert anom_sel.equals(anom_gb) ```

Related: #7759 #7766 cc @dcherian

What did you expect to happen?

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Minimal Complete Verifiable Example

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MVCE confirmation

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

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Environment