Short-Term Climate Prediction over China Mainland: An Attempt Using Machine Learning, Considering Natural and Anthropic Factors

Abstract

Climate extremes pose significant natural threats to socioeconomic activities. Accurate prediction of short-term climate (STC) can provide relevant departments with warnings to effectively reduce this threat. To accurately predict STC in China, this study utilizes machine learning algorithms, particularly the random forest (RF) model, to evaluate the role of both natural and anthropogenic factors. Monthly temperature and precipitation data from 160 meteorological stations spanning China, as well as natural climate factors and an economic activity index, were obtained to perform a seasonal hindcast of air temperature and precipitation observed from 1979 to 2018. Our focus was to predict the seasonal mean temperature and precipitation, specifically the summer (June, July, and August (JJA)) and winter (December, January, and February (DJF)) air temperature and precipitation anomalies using forecast factors from the preceding season. Results show that a comprehensive consideration of both natural and anthropogenic effects provides a more accurate fit to the observed climate trends compared to using only one factor. When both factors were integrated, the model scores (coefficient of determination) exceeded 0.95, close to 1.00, which is significantly higher than those of natural (0.86 for temperature, 0.85 for precipitation) or anthropogenic (0.90 for temperature and 0.50 for precipitation) factors alone. Furthermore, we also attempted to predict similar components for 2019 and 2020. The average relative error between predictions and observations was less than 10%, indicating that this integrated model’s performance exhibited a significant improvement in predicting the STC. The findings of this study underscore the importance of accounting for both natural and anthropogenic factors in predicting climate trends to inform sustainable decision-making in China.