  Forecasting global precipitation patterns and, in particular, extreme
precipitation events is of critical importance to preparing for and adapting to
climate change. Making accurate high-resolution precipitation forecasts using
traditional physical models remains a major challenge in operational weather
forecasting as they incur substantial computational costs and struggle to
achieve sufficient forecast skill. Recently, deep-learning-based models have
shown great promise in closing the gap with numerical weather prediction (NWP)
models in terms of precipitation forecast skill, opening up exciting new
avenues for precipitation modeling. However, it is challenging for these deep
learning models to fully resolve the fine-scale structures of precipitation
phenomena and adequately characterize the extremes of the long-tailed
precipitation distribution. In this work, we present several improvements to
the architecture and training process of a current state-of-the art deep
learning precipitation model (FourCastNet) using a novel generative adversarial
network (GAN) to better capture fine scales and extremes. Our improvements
achieve superior performance in capturing the extreme percentiles of global
precipitation, while comparable to state-of-the-art NWP models in terms of
forecast skill at 1--2 day lead times. Together, these improvements set a new
state-of-the-art in global precipitation forecasting.
