Generative deep learning models are increasingly adopted for climate downscaling due to their ability to efficiently produce high-resolution outputs from coarse-resolution boundary conditions. However, unlike dynamical downscaling models, which explicitly represent physical mechanisms governing regional climate behavior, generative models remain largely opaque in terms of whether and how they reflect known or plausible regional physical processes. This lack of domain knowledge connection and understanding limits scientific trust and hinders the use of generative downscaling in risk-sensitive and decision-making situations. In this work, we present a visual analytics approach that enables domain experts to explore regional physical processes reflected in trained generative climate downscaling models by examining how spatially localized, multivariable input patterns relate to model outputs across cohorts of similar predictions.
Chang et al. (Sat,) studied this question.