Work

Prediction of the onset of the South China Sea summer monsoon based on machine learning

Year: 2025

Type: article

Abstract: ABSTRACT Accurately predicting the onset of the South China Sea (SCS) summer monsoon (SCSSM) is crucial for agricultural planning and disaster prevention for countries surrounding the SCS. However, due to the limited availability of data (only one data point per year), the prediction of the onset date of the SCSSM faces significant uncertainty. In this study, we propose a new forecasting method for the onset date of the SCSSM using the Random Forest regression model called MMPM (Multi-model Prediction Method). The MMPM generates a large number of randomly initialized predictive models (∼10 000) and selects the optimal one based on data from the past nine years to predict the date of SCSSM onset for the following year. By using this method, we effectively mitigate the impact of interdecadal variability and predict the date one month before the onset of the SCSSM using factors such as sea surface temperature, mean sea level pressure, and temperature tendency from the previous few months. Compared to an existing statistical forecasting method (SM-17), the MMPM improves the predictive performance by over 10%. This study demonstrates the effectiveness of an innovative strategy in overcoming data limitations and providing better tools for predicting important weather patterns.

Source: Atmospheric and Oceanic Science Letters

DOI: 10.1016/j.aosl.2025.100676

Authors: Suan Hu, Yineng Li, Jieshuo Xie, Ke Huang, Wenping Gong, Lu Yang