min and max methods changing dtype to float64

I'm working with datasets that have coordinates with lots of decimal places. As I don't need this I reassign cords and transform the dtype to float32.
For calculating the extent of the dataset a standard procedure is to use the min() and max() methods but using them over the float32 coordinates yields the float64 values which leads to misaligned coordinates.

Minimum reproducible example:

import xarray as xr
import numpy as np
 
da = xr.DataArray(
    data=np.array([1.2, 2.2, 3.3], dtype=np.float32),
    coords={"x": [1.345, 2.345, 3.345]},
    dims=("x",),
)
 
print(da[0].dtype)  # dtype('float32')
print(da.min().dtype)  # dtype('float64')
print(type(np.min(da.values)))  # dtype('float32')

Is there a reason for this?

Reproduce the behavior with the Python example, comparing xarray's DataArray.min() and DataArray.max() with NumPy's reductions on float32 data. Trace the reduction path in xarray and verify that the completed change preserves the input dtype for these methods without breaking the reported minimum and maximum values.