Hi @guidocioni.

I see that the longitude and latitude coordinates both have different dtype in the two input datasets, which likely explains why you have many NaNs and larger sizes (almost 2x) for the lat and lon dimensions in the resulting dataset.

Here's a small reproducible example:

```python import numpy as np import xarray as xr

lat = np.random.uniform(0, 40, size=100) lon = np.random.uniform(0, 180, size=100)

ds1 = xr.Dataset( coords={"lon": lon.astype(np.float32), "lat": lat.astype(np.float32)} ) ds2 = xr.Dataset( coords={"lon": lon, "lat": lat} )

ds1.indexes["lat"].equals(ds2.indexes["lat"])

False

xr.merge([ds1, ds2], join="exact")

ValueError: cannot align objects with join='exact' where index/labels/sizes

are not equal along these coordinates (dimensions): 'lon' ('lon',)

```

If coordinates labels differ only by their encoding, you could use xr.merge([ds1, ds2], join="override"), which will take the coordinates from the 1st object.