Localizing regional signals of climate variability using integrated gradients

The accuracy of deep learning models comes at the expense of the interpretability of their results. This creates an imperative to assess the validity of network predictions, particularly in fields requiring complex network architectures coupled with high data granularity for detection or forecasting. In this paper, we demonstrate a model interpretability pipeline using explainable artificial intelligence (XAI) for a climate dataset classification task.