`coords.update()` converts CuPy arrays to NumPy for dimension coordinates

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?

Start by reproducing the minimal example at the DataSet.coords.update() entry point and inspect how the assigned coordinate data is handled. Trace where the CuPy array becomes a NumPy array, then determine the intended behavior and add a regression test showing the resulting coordinate type.