You can test the code in two ways: through Google Colab or locally on your machine.

The Google Colab notebook shows an example of a workflow involving multiple python modules to calculate on-the-fly (more or less) correlation and probabilistic skill scores on a seasonal forecast downloaded by the Copernicus Climate Data Store

The Google Colab notebook can be explored here

Locally, you can install the anaconda environment with: conda env create -f requirements.yml

and then run the notebook with Jupyter.

We use the version 0.7.1 of the module esmpy due to a bug arising in the regridding when using the version 8. See https://github.com/JiaweiZhuang/xESMF/issues/85.