CWRF downscaling with improved land surface initialization enhances spring-summer seasonal climate prediction skill in China

Abstract This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate-Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during seven of ten years with relative high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia-Northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities. Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian High alongside a strengthened East Asian Jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions.