Learning to Correct Climate Projection Biases.

The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post‐process model outputs to match observations, existing approaches usually focus on limited, low‐order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre‐assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle‐consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately 1° resolution, ensures spatiotemporal consistency and inter‐field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big‐data enhanced climate predictions. Plain Language Summary: Accurate climate prediction is crucial for understanding climate change and implementing effective climate adaptation strategies. However, climate models that are used to generate climate predictions have multifaceted biases that often need to be corrected before predictions can be considered usable. We develop a data‐driven methodology that detects and corrects climate model biases using a game theory inspired machine learning technique. By applying physical and statistical constraints, our predictions are not only more accurate, but also more trustworthy as judged by our physical understandings. Key Points: Identify and correct climate projection biases using unpaired climate simulation and observation dataRegularize the data‐driven bias corrector using statistical and dynamical constraintsSignificant improvement of daily precipitation estimation regarding a broad range of spatiotemporal statistics [ABSTRACT FROM AUTHOR]