Understanding the Potential Changes in Causal-Physical Drivers of Extreme Mei-yu Precipitation and Potential Applications

Accurate predictions of extreme Mei-yu precipitation (MYR) over China for near-term and long-term climate is crucial. This is because such information is essential for decision and policy makers to develop optimal strategies to mitigate any negative socioeconomic impact which could be caused by changes in MYR. While the performance of climate models has improved substantially over the past few decades, accurate prediction of MYR remains an open challenge. On the other hand, climate models often have a better representation of the large-scale climate modes (LSCMs) and many studies have suggested some LSCMs and MYR are related. A recent study has demonstrated the representation of MYR in climate models can be improved by using causality-guided statistical models (CGSMs) based on LSCMs causally related to MYR as predictors. However, the potential changes in these causal-physical drivers on (multi-)decadal timescale has not previously been considered. In this presentation, we present the preliminary results on the potential changes in causal-physical drivers, which govern MYR, on (multi-)decadal timescales. A potential application of such information for decadal prediction systems is also discussed.