issue_comments: 747376047

I just need to find the three warmest consecutive months from a temperature dataset for my work, so I thought I add a complete example. First, create an example dataset with monthly temperature:

```python import xarray as xr import numpy as np import pandas as pd

time = pd.date_range("2000", periods=12 * 30, freq="M") temp = np.sin((time.month - 5) / 6 * np.pi) + np.random.randn(*time.shape) * 0.3 da = xr.DataArray(temp, dims=["time"], coords=dict(time=time)) print(da) ```

python <xarray.DataArray (time: 360)> array([-0.676731, -0.812742, -1.367547, ..., 0.186731, 0.237676, -0.343879]) Coordinates: * time (time) datetime64[ns] 2000-01-31 2000-02-29 ... 2029-12-31

Currently we can achieve this like:

```python n_months = 3

monthly = da.groupby("time.month").mean() padded = monthly.pad(month=n_months, mode="wrap") rolled = padded.rolling(center=True, month=n_months).mean(skipna=False) sliced = rolled.isel(month=slice(3, -3))

central_month = sliced.idxmax() ```

Implementing pad_mode in rolling would allow to do:

```python monthly = da.groupby("time.month").mean() rolled = monthly.rolling(center=True, month=n_months, pad_mode="wrap").mean(skipna=False)

central_month = rolled.idxmax() ```