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Methodologies 1. Attribution methods Some methods contribute to the explanation of Deep Learning (DL) models by computing the importance/relevance of input features, like Local Interpretable Model-ggnostic Explanations (LIME), Shapley Additive Explanation (SHAP), etc. Some methods utilize network gradients, like saliency maos, Deconvolutional Neural Networks (DeConvNet), Layer-Wise Relevance Propagation (LRP), Pattern Attribution, and Randomized Input Sampling for Explanation (RISE), etc. 2. Ante-hoc explainable models Traditional Machine Learning (ML) methods are often adopted for ante-hoc methods, especially techniques based on Decision Trees (DTs). Anothor recent development in XAI is rule-based approaches. ...

Related Work 1. ClimateLearn [arxiv GitHub] There are three tasks in ClimateLearn: Weather forecastiing, downscaling, and climate projection. Datasets included: ERA5, CMIP6, PRISM Models included: Baseline (Climatology, Persistence, Interpolation, Linear regression), and Deep Learning Models (ResNet, U-Net, Vision Transformer).