Global Precipitation Correction Across a Range of Climates Using CycleGAN

Abstract

Abstract Accurate precipitation simulations for various climate scenarios are critical for understanding and predicting the impacts of climate change. This study employs a Cycle‐generative adversarial network (CycleGAN) to improve global 3‐hr‐average precipitation fields predicted by a coarse grid (200 km) atmospheric model across a range of climates, morphing them to match their statistical properties with those of reference fine‐grid (25 km) simulations. We evaluate its performance on both the target climates and an independent ramped‐SST simulation. The translated precipitation fields remove most of the biases simulated by the coarse‐grid model in the mean precipitation climatology, the cumulative distribution function of 3‐hourly precipitation, and the diurnal cycle of precipitation over land. These results highlight the potential of CycleGAN as a powerful tool for bias correction in climate change simulations, paving the way for more reliable predictions of precipitation patterns across a wide range of climates.

Keywords

• machine learning
• cyclegan
• bias correction
• neural network
• gan