Evaluation of High-Resolution Downscaling Predictions for the July 2023 Extreme Rainstorm in the Beijing-Tianjin-Hebei Region Based on CMA-CPSv3

Abstract

The integration of weather and climate prediction represents the current frontier in the development of numerical modeling in China. Dynamic downscaling serves as a pivotal approach, improving the performance and resolution of global climate models to the weather scale. Focusing on the ‘23.7’ extreme rainstorm (July 29, 00:00–August 2, 00:00 UTC) in the Beijing-Tianjin-Hebei region, this study assesses predictions from the China Meteorological Administration Climate Prediction System version 3 (CMA-CPSv3, 45 km resolution) and 9-km dynamic downscaling hindcasts from the Weather Research and Forecasting model (WRF-9 km). In contrast to the conventional climate anomaly approaches, direct outputs are used for evaluation, similar to weather forecasting tests. By examining, both the CMA-CPSv3 predictions and the WRF-9 km hindcasts provide a 5-day prediction window for this rainstorm. They successfully predict the rainstorms and related atmospheric circulations from July 24th onward, aligning with observed and reanalyzed data. WRF-9 km, with the higher resolution and optimised physical processes, outperforms CMA-CPSv3, especially in precipitation spatial distribution and center intensity. The WRF-9 km 7/24 hindcast demonstrates the most significant enhancement compared to the corresponding CMA-CPSv3 prediction. This improvement is notably reflected in the substantial increase in spatial correlation, from 0.68 to 0.79, as well as a reduction in the difference of center values, decreasing from −51% to −20%. Furthermore, the WRF-9 km 7/24 hindcast improves the Critical Success Index by 0.08, the Success Rate by 0.08, and the Probability of Detection by 0.29 for heavy rainfall (over 25.0 mm/d). However, improvements in large-scale circulations with WRF-9 km are limited, which may restrict advancements in predictability. In conclusion, the WRF-9 km enhances the performance and resolution of CMA-CPSv3 predictions, which can be regarded as a viable pathway for CMA-CPSv3 to achieve weather-climate integration.