cumulate+argmax uses padded index instead of absolute index

[saschahofmann]

What happened?

The combination of cumulative + argmax does not lead to the (at least from me) expected result.
Example:

import numpy as np
import xarray as xr
da = xr.DataArray([1,2,1.5,3.5,4], coords={'time': ('time', np.arange(5))})
da.cumulative('time').argmax()
# [4., 4., 3., 4., 4.]
# expected [0, 1, 1,3,4]

If I want to fetch the time for the largest value until that value I would use something like above cumulative('time').argmax(). However it doesnt not return the index but seems to use some kind of padded rolling array and fetches the argmax from this. Maybe this is the expected behaviour but I was definitely suprised.

MVCE confirmation

• Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• Complete example — the example is self-contained, including all data and the text of any traceback.
• Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• New issue — a search of GitHub Issues suggests this is not a duplicate.
• Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Environment

Details

INSTALLED VERSIONS

commit: None
python: 3.13.8 | packaged by conda-forge | (main, Oct 13 2025, 14:15:33) [GCC 14.3.0]
python-bits: 64
OS: Linux
OS-release: 5.14.0-427.22.1.el9_4.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: ('C', 'UTF-8')
libhdf5: 1.14.6
libnetcdf: 4.9.3

xarray: 2025.10.1
pandas: 2.3.3
numpy: 2.3.3
scipy: 1.16.2
netCDF4: 1.7.3
pydap: None
h5netcdf: None
h5py: None
zarr: 2.18.7
cftime: 1.6.4
nc_time_axis: None
iris: None
bottleneck: 1.6.0
dask: 2025.10.0
distributed: 2025.10.0
matplotlib: 3.10.6
cartopy: 0.24.0
seaborn: 0.13.2
numbagg: None
fsspec: 2025.9.0
cupy: None
pint: 0.25
sparse: 0.17.0
flox: 0.10.7
numpy_groupies: 0.11.3
setuptools: 80.9.0
pip: 25.3
conda: None
pytest: 8.4.2
mypy: None
IPython: 9.6.0
sphinx: 7.4.7