Seasonal Forecasts of Precipitation during the First Rainy Season in South China Based on NUIST-CFS1.0

Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China. This study evaluates seasonal forecast skill for precipitation in the first rainy season (FRS, i.e., April-June) over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously known as SINTEX-F). The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general. But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index. NUIST-CFS1.0 captures the anomalous Philippines anticyclone, which transports moisture and heat northward to South China, favoring more precipitation in South China during the FRS. By examining the correlations between sea surface temperature (SST) and FRS precipitation and the Philippines anticyclone, we find that the model reasonably captures SST-associated precipitation and circulation anomalies, which partly explains the predictability of FRS precipitation. A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events. Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts. These results help improve the understanding and forecasts for FRS precipitation in South China.

Automated summary

Current dynamical models struggle to provide accurate seasonal forecasts of regional/local rainfall in South China. This study evaluates the skill of the NUIST-CFS1.0 model in predicting precipitation during the first rainy season from 1982 to 2020. Despite low overall predictability, NUIST-CFS1.0 outperforms other international models in terms of correlation coefficient skill for interannual precipitation anomalies and associated circulation patterns. It successfully captures the anomalous Philippines anticyclone, which plays a role in transporting moisture and heat to South China and influencing precipitation patterns.