Interestingly, xarray is also perfectly happy to read a numpy.datetime64 array out of a zarr store as long as the xarray metadata is present. xarray even helpfully creates an '_FillValue" attribute for the array so there is no confusion:

```

Create a zarr store directly with numpy.datetime64 type

location_zarr_direct = "from_zarr.zarr" root = zarr.open(location_zarr_direct,mode='w') z_time_array = root.create_dataset( "time",data=time,shape=time.shape,chunks=time.shape,dtype=time.dtype, fill_value=time_fill_value )

Add xarray metadata

z_time_array.attrs["_ARRAY_DIMENSIONS"] = ["time"] zarr.convenience.consolidate_metadata(location_zarr_direct)

Use xarray to read this data out

xr_read_from_zarr = xr.open_zarr(location_zarr_direct) print(xr_read_from_zarr["time"]) <xarray.DataArray 'time' (time: 2)> array([ 'NaT', '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] NaT 2023-01-02 Attributes: _FillValue: NaT ```

So I am extremely confused as to why xarray encodes time arrays so strangely when it creates the zarr store itself! (Hence https://github.com/pydata/xarray/discussions/7776)