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<p>Demand for accurate forecasts of long-lasting heatwaves is increasing, yet the processes that fundamentally limit their predictability remain poorly understood. Here we study the predictability of the August 2022 Yangtze River Valley (YRV) heatwave using real-time subseasonal to seasonal (S2S) forecast ensembles, which systematically underestimate this event’s intensity. To identify factors limiting predictability, we analyze processes across multiple scales, including large-scale circulation anomalies, local convection, and soil-moisture conditions. Analysis shows that regional precipitation effectively separates high- and low-skill forecasts of surface maximum temperature. Quantitative attribution analysis indicates that precipitation, rather than the local highs, explains most of the ensemble spread in surface temperature, suggesting precipitation–soil moisture coupling as the dominant limiting factor. Numerical experiments using a simple heatwave model demonstrate that increased precipitation reduces surface warming by up to ~4 °C, whereas reduced precipitation amplifies warming by ~2 °C. These findings underscore the importance of improving representation of precipitation processes&#xa0;to enhance heatwave predictability.</p>

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Precipitation and soil moisture coupling constrains subseasonal predictability of a prolonged extreme heatwave

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月28日

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  • Bingjie Lv,
  • Shuguang Wang,
  • Gang Chen,
  • Baoqiang Xiang

摘要

Demand for accurate forecasts of long-lasting heatwaves is increasing, yet the processes that fundamentally limit their predictability remain poorly understood. Here we study the predictability of the August 2022 Yangtze River Valley (YRV) heatwave using real-time subseasonal to seasonal (S2S) forecast ensembles, which systematically underestimate this event’s intensity. To identify factors limiting predictability, we analyze processes across multiple scales, including large-scale circulation anomalies, local convection, and soil-moisture conditions. Analysis shows that regional precipitation effectively separates high- and low-skill forecasts of surface maximum temperature. Quantitative attribution analysis indicates that precipitation, rather than the local highs, explains most of the ensemble spread in surface temperature, suggesting precipitation–soil moisture coupling as the dominant limiting factor. Numerical experiments using a simple heatwave model demonstrate that increased precipitation reduces surface warming by up to ~4 °C, whereas reduced precipitation amplifies warming by ~2 °C. These findings underscore the importance of improving representation of precipitation processes to enhance heatwave predictability.