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On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates
Neelesh Rampal, Peter B. Gibson, Steven Sherwood, Gab Abramowitz
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While deep-learning downscaling algorithms can generate fine-scale climate projections cost-effectively, it is still unclear how well they will extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic Convolutional Neural Network baseline and a Generative Adversarial Network (GAN) built with this baseline, trained to predict daily precipitation simulated by a Regional Climate Model (RCM). Both approaches emulate future changes in annual mean precipitation well, even when trained on historical data, though training on a future climate improves performance. For extreme precipitation (99.5th percentile), RCM simulations predict a robust end-of-century increase with future warming (~5.8%/{\deg}C on average from five simulations). When trained on a future climate, GANs capture 97% of the warming-driven increase in extreme precipitation compared to 65% in a deterministic baseline. Even GANs trained historically capture 77% of this increase. Overall, GANs offer better generalization for downscaling extremes, which is important in applications relying on historical data.
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