Thanks for that note. I have a bunch of variables, like precipitation type, where that would be totally fine. Definitely looking to save on disk space, so may try to recompute the scale_factor and add_offset on other variables as suggested.

Thanks for the alternative @veenstrajelmer. I'll give it a try on my end.

Thanks for flagging the issue again. I've been using the same workaround of removing the dtype before writing to a zarr/netcdf. It's an extra step but has worked for me so far.

One last observation. If I just remove dtype from the original encoding and apply it to the dataset before writing to a netcdf, it works fine. Otherwise, I have the issue if I leave dtype in.

```python

This works

encoding = {'original_shape': (720, 109, 245), 'missing_value': -32767, '_FillValue': -32767, 'scale_factor': 0.0009673806360857793, 'add_offset': 282.08577424425226} python

This does not work

encoding = {'original_shape': (720, 109, 245), 'missing_value': -32767, 'dtype': 'int16', # the original form says it should be 'dtype': dtype('int16'), but this causes an error for me, whereas this form works fine to change between data types '_FillValue': -32767, 'scale_factor': 0.0009673806360857793, 'add_offset': 282.08577424425226} ```

That figure is basically what I am getting. Perhaps I designed the MRE poorly, however, I am curious as to what exactly from the encoding introduces the noise (I still need to read through the documentation more thoroughly)? If I don't apply the original encoding, I get a straight line at 0 for the difference plot.

With that being said, if you are willing to try a test with the actual ERA5 data, I've attached it here via a box link. I went back and figured out I need at least several files to get large differences. Oddly enough, if I use only 2 files, the difference looks more like noise (+/- 0.0005). If I only open a single file, no difference. If I add a couple more files, the differences become quite large.

Data: https://epri.box.com/s/spw9plf77lrjj1xz2spmwd34b5ls9dea

```python import xarray as xr import matplotlib.pyplot as plt

Open original time series

ERA5_t2m = xr.open_mfdataset(r'...\Test\T2m_*' + '.nc') # open 4 files

Save time series as netcdf

ERA5_t2m.to_netcdf(r"...\Test\Phx_Temperature_to_netcdf.nc") # save 4 files

open bad netcdf

ERA5_t2m_bad = xr.open_dataset(r'...\Test\Phx_Temperature_to_netcdf.nc')

Lat and lon for Phx

lats = [33.35] lons = [-112.86]

plot the difference between the same point from the two files

plt.plot(ERA5_t2m.t2m.sel(latitude = lats[0], longitude = lons[0], method='nearest') - ERA5_t2m_bad.t2m.sel(latitude = lats[0], longitude = lons[0], method='nearest')) ```

Thanks for the explanation. Makes a lot more sense now! All figures I've attached are from the real ERA5 data. The figure I attached in my most recent comment with the alternative MRE (with the ERA5 data) is what I get when I run that code with the data I provided in the test folder.