@Michael_Barnes, it looks like you’re happy with the cdo solution, which is great. But, just to add to my previous answer, using dask and appropriate chunking will help here (sorry, I didn’t originally realize the size of your input data).
For example, your initial code took me approximately 12 mins to run using a “Large” ARE instance (7 cpus, 32GB mem). The following takes 1.5 mins:
import xarray as xr
from distributed import Client
Client()
tfile = "/g/data/rt52/era5/pressure-levels/reanalysis/t/2022/t_era5_oper_pl_20220301-20220331.nc"
hourly = xr.open_dataset(tfile, chunks={"time": 24, "level": 2})
daily = hourly.coarsen(time=24).mean(keep_attrs=True).compute()
Is this competitive with using cdo?
Note, using resample instead of coarsen above takes approximately 3 mins.