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<p>The climate system exhibits intricate correlations and hysteresis effects due to the interplay of multiple factors. This study presents a middle-long term monthly precipitation forecasting model in the Asian monsoon region, considering lagged climate indices as predictors based on machine learning. The key findings are as follows: (i) Over the past century, the Asian monsoon region has experienced a significant increase in average annual precipitation, with extreme precipitation events predominantly occurring near the leading edge of the monsoon. (ii) Notable lagged relationships between precipitation and climatic indices such as temperature, evapotranspiration, vapor pressure, and various oscillation indices have been identified through this investigation. (iii) By incorporating six factors into proposed model inputs, we achieve accurate monthly average precipitation forecasts up to five months in advance, surpassing predictions derived from other combined inputs and CMIP6 Global Climate Models. These findings provide innovative insights and methodologies for improving precipitation forecasting, offering valuable references for addressing climate change impacts, water resource management and environmental protection in the Asian monsoon region.</p>

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Integration of local climatic factors and global climate oscillations for enhanced monthly precipitation forecasting in the Asian monsoon region

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Congcong Li,
  • Lulu He,
  • Lin Luo,
  • Dan Liu,
  • Junxia Liao

摘要

The climate system exhibits intricate correlations and hysteresis effects due to the interplay of multiple factors. This study presents a middle-long term monthly precipitation forecasting model in the Asian monsoon region, considering lagged climate indices as predictors based on machine learning. The key findings are as follows: (i) Over the past century, the Asian monsoon region has experienced a significant increase in average annual precipitation, with extreme precipitation events predominantly occurring near the leading edge of the monsoon. (ii) Notable lagged relationships between precipitation and climatic indices such as temperature, evapotranspiration, vapor pressure, and various oscillation indices have been identified through this investigation. (iii) By incorporating six factors into proposed model inputs, we achieve accurate monthly average precipitation forecasts up to five months in advance, surpassing predictions derived from other combined inputs and CMIP6 Global Climate Models. These findings provide innovative insights and methodologies for improving precipitation forecasting, offering valuable references for addressing climate change impacts, water resource management and environmental protection in the Asian monsoon region.