• [x] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst

Small follow up to #8877. If we're going to drop the indices anyways for region writes, we may as well not raise if they are still in the dataset. This makes the user experience of region writes simpler:

```python ds = xr.tutorial.open_dataset("air_temperature") ds.to_zarr("test.zarr") region = {"time": slice(0, 10)}

This fails unless we remember to ds.drop_vars(["lat", "lon"])

ds.isel(**region).to_zarr("test.zarr", region=region) ```

I find this annoying because I often have a dataset with a bunch of unrelated indexes and have to remember which ones to drop, or use some verbose set logic. I thought #8877 might have already done this, but not quite. By just reordering the point at which we drop indices, we can now skip this. We still raise if data vars are passed that don't overlap with the region.

cc @dcherian

• [x] Closes #6891
• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

• [x] Closes #4554
• [x] Closes #5629
• [x] Closes #5644
• [ ] Tests added
• [x] Passes pre-commit run --all-files
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst

Reimplement polyfit using apply_ufunc rather than dask.array.linalg.lstsq. This should solve a number of issues with memory usage and chunking that were reported on the current version of polyfit. The main downside is that variables chunked along the fitting dimension cannot be handled with this approach.

There is a bunch of fiddly code here for handling the differing outputs from np.polyfit depending on the values of the full and cov args. Depending on the performance implications, we could simplify some by keeping these in apply_ufunc and dropping later. Much of this parsing would still be required though, because the only way to get the covariances is to set cov=True, full=False.

A few minor departures from the previous implementation: 1. The rank and singular_values diagnostic variables returned by np.polyfit are now returned on a pointwise basis, since these can change depending on skipped nans. np.polyfit also returns the rcond used for each fit which I've included here. 2. As mentioned above, this breaks fitting done along a chunked dimension. To avoid regression, we could set allow_rechunk=True and warn about memory implications. 3. Changed default skipna=True, since the previous behavior seemed to be a limitation of the computational method. 4. For consistency with the previous version, I included a transpose operation to put degree as the first dimension. This is arbitrary though, and actually the opposite of how curvefit returns ordering. So we could match up with curvefit but it would be breaking for polyfit.

No new tests have been added since the previous suite was fairly comprehensive. Would be great to get some performance reports on real-world data such as the climate model detrending application in #5629.