issues: 566311296

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
566311296 MDU6SXNzdWU1NjYzMTEyOTY= 3773 Downsampling with resample generates longer time array than expected 9004403 open 0     0 2020-02-17T13:48:49Z 2022-01-11T22:06:33Z   NONE      

MCVE Code Sample

```python import xarray as xr import pandas as pd from functools import reduce

Create time dimension array for all climatological winters

indexes = [pd.DatetimeIndex(f'{year}-12-01', f'{year+1}-03-01', freq='12H') for year in range(1980, 2020)] index_union = reduce(pd.Index.union, indexes)

Create DataArray

ds = xr.Dataset({'var': ('time', np.arange(len(ix_union))), 'time': index_union}) ```

Problem Description

From here we can check ds time dimension months: ```python

pd.DatetimeIndex(ds.time.values).month.unique() Int64Index([12, 1, 2, 3], dtype='int64') ```

Now, if we downsample our Dataset to a one week period: ```python

pd.DatetimeIndex(ds.resample(time='1W').mean().time.values).month.unique() Int64Index([12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype='int64') ```

Instead of getting the weeks within the months in the original Dataset, we obtain additional months with missing values in the var array. One way of solving this issue is to use the dropna() method, but is a slow approach.

Expected Output

We would expect something like this:

```python

pd.DatetimeIndex(ds.resample(time='1W').mean().time.values).month.unique() Int64Index([12, 1, 2, 3], dtype='int64') ```

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.7.6 (default, Jan 8 2020, 19:59:22) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 3.10.0-957.12.2.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.3 xarray: 0.14.1 pandas: 0.25.3 numpy: 1.17.5 scipy: 1.4.1 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: 0.9.7.7 iris: 2.3.0 bottleneck: None dask: 2.9.2 distributed: 2.9.3 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None setuptools: 44.0.0.post20200106 pip: 19.3.1 conda: None pytest: None IPython: 7.11.1 sphinx: None 
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     13221727 issue

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