Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

XAI techniques reveal hidden model behaviors beyond standard metrics: The paper demonstrates that explainability methods uncover how deep learning models actually make predictions for climate downscaling, providing evaluation insights that standard validation approaches miss—particularly important when assessing whether models will work reliably under future climate conditions.

Model complexity requires enhanced evaluation for climate applications: Because deep learning models are inherently complex "black boxes," the authors argue that proper evaluation is crucial when applying these tools to climate projections, where the ability to generalize to changing conditions (rather than just fitting historical data) directly impacts the reliability of regional climate forecasts.

XAI helps identify which models generalize better to climate change: By examining model internals through explainability techniques, researchers can better diagnose which deep learning approaches will extrapolate appropriately when climate conditions shift beyond the training data—a critical concern for using these models to inform climate adaptation decisions.

Explainability should be integrated into climate model evaluation frameworks: The paper recommends incorporating XAI techniques as standard practice when evaluating statistical downscaling models, especially for large geographic regions or scenarios involving significant climate change, suggesting this represents a best practice for responsible deployment of AI in climate science.