`.sel()` fails on `datetime64[s]` object

What happened?

Hi there,

sorry if this might be a duplicate, but I have been browsing the repo without finding anything specific which resemble this.

So, I am exploring to the possibility of calling xarray with CFDatetimeDecoder on time period overshooting pandas threshold year 2262
Running with xarray=2025.9.0

import xarray as xr
files="/ec/res4/scratch/ecme3497/ece4/pic2/output/oifs/pic2_atm_cmip6_1m_24*.nc"
coder = xr.coders.CFDatetimeCoder(time_unit='s')
data = xr.open_mfdataset(files, decode_times=coder)
data.time_counter

which gives me

array(['2400-01-16T12:00:00', '2400-02-15T12:00:00', '2400-03-16T12:00:00',
       ..., '2489-10-16T12:00:00', '2489-11-16T00:00:00',
       '2489-12-16T12:00:00'], shape=(1080,), dtype='datetime64[s]')

so far so good. However

data.sel(time_counter=slice("2400-01-01", "2420-01-01"))

OverflowError                             Traceback (most recent call last)
File pandas/_libs/tslibs/period.pyx:1169, in pandas._libs.tslibs.period.period_ordinal_to_dt64()

OverflowError: Overflow occurred in npy_datetimestruct_to_datetime

The above exception was the direct cause of the following exception:

OutOfBoundsDatetime                       Traceback (most recent call last)
Cell In[8], [line 1](vscode-notebook-cell:?execution_count=8&line=1)
----> [1](vscode-notebook-cell:?execution_count=8&line=1) data.sel(time_counter=slice("2400-01-01", "2420-01-01"))

File /ECMWF_kQYjfeo/miniforge/envs/env1/lib/python3.12/site-packages/xarray/core/dataset.py:2974, in Dataset.sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
   2906 """Returns a new dataset with each array indexed by tick labels
   2907 along the specified dimension(s).
   2908 
   (...)   2971 
   2972 """
   2973 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel")
-> [2974](https://vscode-remote+ssh-002dremote-002bhpc-002dlogin.vscode-resource.vscode-cdn.net/ECMWF_kQYjfeo/miniforge/envs/env1/lib/python3.12/site-packages/xarray/core/dataset.py:2974) query_results = map_index_queries(
   2975     self, indexers=indexers, method=method, tolerance=tolerance
   2976 )
   2978 if drop:
   2979     no_scalar_variables = {}
...
File pandas/_libs/tslibs/period.pyx:1992, in pandas._libs.tslibs.period._Period.to_timestamp()

File pandas/_libs/tslibs/period.pyx:1172, in pandas._libs.tslibs.period.period_ordinal_to_dt64()

OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2400-01-01 00:00:00

I know this can be fixed using cftime, but I though that using datetime64[s] would have fix this issue. In general, I am quite confused on what are the currently supported features of xarray for going beyond the the nanoseconds limitation of pandas. Shall we keep working with cftime? Is there something I am missing? To what extent can we used the Coder if this is the output?
Any help, also suggestion to documentation, is greatly appreciated!
Thanks a lot

EDIT: I can upload some of the data required, but below you can find a reproducible example

What did you expect to happen?

As expected, correct time selection.

Minimal Complete Verifiable Example

import numpy as np
import xarray as xr

# monthly starts from 2389-01 to 2489-12 (inclusi)
months = np.arange("2389-01", "2490-01", dtype="datetime64[M]")  # dtype M = month starts
times = months.astype("datetime64[s]")  # convert to seconds (YYYY-MM-01T00:00:00)
# creare DataArray di esempio
da = xr.DataArray(np.zeros(times.size), coords={"time": times}, dims=["time"])
da.sel(time=slice("2400-01-01", "2420-01-01"))

I have been able to replicate this also in most recent version of xarray

>>> xr.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.14.0 | packaged by conda-forge | (main, Dec  2 2025, 20:23:19) [Clang 20.1.8 ]
python-bits: 64
OS: Darwin
OS-release: 23.6.0
machine: arm64
processor: arm
byteorder: little
LC_ALL: None
LANG: it_IT.UTF-8
LOCALE: ('it_IT', 'UTF-8')
libhdf5: None
libnetcdf: None

xarray: 2025.11.0
pandas: 2.3.3
numpy: 2.3.5
scipy: None
netCDF4: None
pydap: None
h5netcdf: None
h5py: None
zarr: None
cftime: None
nc_time_axis: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: None
cartopy: None
seaborn: None
numbagg: None
fsspec: None
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: None
pip: 25.3
conda: None
pytest: None
mypy: None
IPython: None
sphinx: None

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

xarray>=2025.9.0

Start with the provided NumPy/xarray MVCE and trace the time-coordinate path used by DataArray.sel for string slices and datetime64[s] values. Confirm the failure against the shown pandas traceback; done means the 2400–2420 selection succeeds without requiring cftime and is covered by a regression test.