Li Chao, Liu Jieyu, Du Fujun, Zwiers Francis W, Feng Guolin
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China.
School of Geographic Sciences, East China Normal University, Shanghai, China.
Nat Commun. 2025 Jan 20;16(1):850. doi: 10.1038/s41467-025-56235-9.
The latest climate models project widely varying magnitudes of future extreme precipitation changes, thus impeding effective adaptation planning. Many observational constraints have been proposed to reduce the uncertainty of these projections at global to sub-continental scales, but adaptation generally requires detailed, local scale information. Here, we present a temperature-based adaptative emergent constraint strategy combined with data aggregation that reduces the error variance of projected end-of-century changes in annual extremes of daily precipitation under a high emissions scenario by >20% across most areas of the world. These improved projections could benefit nearly 90% of the world's population by permitting better impact assessment and adaptation planning at local levels. Our physically motivated strategy, which considers the thermodynamic and dynamic components of projected extreme precipitation change, exploits the link between global warming and the thermodynamic component of extreme precipitation. Rigorous cross-validation provides strong evidence of its reliability in constraining local extreme precipitation projections.
最新的气候模型预测，未来极端降水变化的幅度差异很大，从而阻碍了有效的适应规划。人们提出了许多观测约束条件，以减少全球到次大陆尺度上这些预测的不确定性，但适应通常需要详细的局部尺度信息。在这里，我们提出了一种基于温度的自适应紧急约束策略，并结合数据聚合，在高排放情景下，将全球大部分地区预计的本世纪末日降水量年度极值变化的误差方差降低了20%以上。这些改进后的预测可以使全球近90%的人口受益，因为它们可以在地方层面进行更好的影响评估和适应规划。我们基于物理原理的策略考虑了预计极端降水变化的热力学和动力学成分，利用了全球变暖和极端降水热力学成分之间的联系。严格的交叉验证有力地证明了其在约束局部极端降水预测方面的可靠性。
