@dcherian I tried to reproduce, with this minimal example I couldn't, so I'm closing the issue. ```python

import xarray as xr import numpy as np

n0 = 10 n1 = 3 x1 = xr.DataArray(np.empty((n0,n1),dtype=np.float64),dims=('dim0','dim1')).chunk({'dim0':2}) x2 = xr.DataArray(np.empty(n0,dtype=bool),dims=('dim0',)).chunk({'dim0':2})

n2 = 10

def f(x1,x2): return np.empty(n2,dtype=x1.dtype),np.empty(n2,dtype=np.min_scalar_type(n2))

m,w = xr.apply_ufunc( f, x1,x2, input_core_dims=[('dim0','dim1'),('dim0',)], output_core_dims=[('dim2',),('dim2',)], vectorize=True, dask='parallelized', dask_gufunc_kwargs={ 'output_sizes':{'dim2':n2}, 'allow_rechunk':True,

'meta':(np.empty((M,),dtype=p.dtype),np.empty((M,),dtype=np.min_scalar_type(M)))

'output_dtypes':[p.dtype,np.min_scalar_type(M)],

},
output_dtypes=[x1.dtype,np.min_scalar_type(n2)] # now works

output_dtypes=(x.dtype,np.min_scalar_type(ny)) # works

) m.compute(),w.compute() (<xarray.DataArray (dim2: 10)> array([1.e-323, 2.e-323, 3.e-323, 4.e-323, 5.e-323, 6.e-323, 7.e-323, 8.e-323, 9.e-323, 1.e-322]) Dimensions without coordinates: dim2, <xarray.DataArray (dim2: 10)> array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8) Dimensions without coordinates: dim2) ```