Enhancing Subseasonal Climate Predictions through Dynamical Downscaling: A Case Study in the Southern Plains of the United States

Predicting extreme precipitation events at subseasonal timescales is a critical challenge in Earth system science. This study advances climate predictability by employing dynamical downscaling, specifically focusing on convection-permitting modeling in the Southern Plains of the United States. Two contrasting extreme precipitation periods in Texas, the extremely dry May of 2011 and the abnormally wet May of 2015, were selected for analysis. To enhance subseasonal climate forecasting, we integrated the Weather Research and Forecasting (WRF) model with the decadal climate prediction system based on the Community Earth System Model (CESM). Evaluating the impact of dynamical downscaling on the prediction of extreme precipitation events, our study demonstrates how high-resolution downscaling enhances model skill in capturing these events. The findings hold the potential to significantly contribute to improving climate predictions and assessing regional climate-related risks, aligning with the session's goals.