Employing Deep Learning to Quantify the Trends in Tropical Cyclones and Associated Extreme Precipitation Events in Southern China

Extreme wind and precipitation events result in signicant societal disruption in the South China coastal region, typically triggered by tropical cyclones (TCs) or mesoscale storms. The large-, meso-, and small-scale atmospheric circulation processes that can influence these high- impact weather events may be altered by climate change, potentially changing TC characteristics. However, quantifying the sensitivity of TCs and extreme precipitation to climate change is challenging, primarily due to the limited detail provided by global model simulations with coarse resolution. High-resolution simulations are essential to address such issues. We have developed a smart dynamical downscaling (SDD) model to downscale the climate simulations (100 km) to high-resolution simulations (15 km). The trained SDD model can be applied to ensemble climate simulations under the SSP585 scenario from 2020 to 2100 to explore the variations of the severe TC cases, regarding the spatial distribution, maximum surface wind, and precipitation, respond to global warming. We found the inland areas of China will be affected more by TC-induced extreme precipitation and the intense typhoons are increased in the future based on the ensemble downscale results. The high-resolution simulations are conducted for selected extreme precipitation events to further under the dynamical response to global warming.