issue_comments: 395082238

When the time coordinate contains np.datetime64 objects I recommend using resample directly, because the underlying index will be a pandas DatetimeIndex (so you just need some logic to detect if that's the case).

I think the most general workaround for right now would probably look something like the example below. This has the property that it preserves the underlying calendar type of the time index. ```python import pandas as pd import xarray as xr

def resample_ms_freq(ds, dim='time'): """Resample the dataset to 'MS' frequency regardless of the calendar used.

Parameters
----------
ds : Dataset
    Dataset to be resampled
dim : str
    Dimension name associated with the time index

Returns
-------
Dataset
"""
index = ds.indexes[dim]
if isinstance(index, pd.DatetimeIndex):
    return ds.resample(**{dim: 'MS'}).mean(dim)
elif isinstance(index, xr.CFTimeIndex):
    date_type = index.date_type
    month_start = [date_type(date.year, date.month, 1) for date in ds[dim].values]
    ms = xr.DataArray(month_start, coords=ds[dim].coords)
    ds = ds.assign_coords(MS=ms)
    return ds.groupby('MS').mean(dim).rename({'MS': dim})
else:
    raise TypeError(
        'Resampling to month start frequency requires using a time index of either '
        'type pd.DatetimeIndex or xr.CFTimeIndex.')

with xr.set_options(enable_cftimeindex=True): ds = xr.open_mfdataset(files) resampled = resample_ms_freq(ds) ```