coords.update() converts CuPy arrays to NumPy for dimension coordinates #11251
Description
What is your issue?
When assigning CuPy arrays as coordinates to a dataset using DataSet.coords.update(), the resulting coordinate data is converted to a NumPy array.
As a result, GPU-native workflows that are dependent on coordinates (e.g. grid interpolation) become CPU-bound or require additional data transfers to the GPU.
Minimal example:
data_cp = cp.arange(6)
ds = xr.Dataset({
"var": (("x"), data_cp)
})
x_cp = cp.array([0,1,2,3,4,5])
print(type(x_cp)) # <class 'cupy.ndarray'>
ds.coords.update({"x": x_cp.copy()})
print(type(ds.coords["x"].data)) # <class 'numpy.ndarray'>xarray: 2026.2.0
Question
Is this conversion intended, or should CuPy-backed coordinates be supported?