近日，美国德州农工大学Ping Chang团队揭示了未来极端降水被中尺度水汽辐合增强放大。相关论文于2025年11月18日发表在《自然—地球科学》杂志上。

极端降水事件是由复杂的多尺度大气动力相互作用驱动的，并由有效水分推动。预计它们将随着气候变化而加剧，对人类社区和生态系统构成越来越大的风险。然而，目前的低分辨率气候模式难以准确地表示关键的极端降水产生现象，限制了它们产生准确和可靠的未来预测的能力。

研究组提出了一个具有10至25公里分辨率和改进的中尺度对流系统表示的气候模拟集合，以评估每日极端降水的未来变化及其驱动因素。他们的高分辨率模拟比100公里分辨率模拟更真实地捕捉了历史时期观测到的每日极端降水的空间分布和强度。在高二氧化碳排放的未来情景中，到2100年，陆地上的日极端降水可能增加约41%，这主要是由于中尺度水汽辐合增加。在低分辨率模式中，这种对极端降水的动力贡献的影响被低估了三倍。这些结果突出了高分辨率气候模拟在限制未来极端事件和为更有效的气候风险评估和适应战略提供信息方面的关键作用。

附：英文原文

Title: Future extreme precipitation amplified by intensified mesoscale moisture convergence

Author: Chang, Ping, Fu, Dan, Liu, Xue, Castruccio, Frederic S., Prein, Andreas F., Danabasoglu, Gokhan, Wang, Xiaoqi, Bacmeister, Julio, Zhang, Qiuying, Rosenbloom, Nan, King, Teagan, Bates, Susan C.

Issue&Volume: 2025-11-18

Abstract: Extreme precipitation events are driven by complex multiscale atmospheric dynamic interactions, fuelled by available moisture. They are expected to intensify with climate change, posing increasing risks to human communities and ecosystems. However, current low-resolution climate models struggle to accurately represent key extreme precipitation-generating phenomena, limiting our ability to generate robust and reliable future projections. Here we present an ensemble of climate simulations with a 10-to-25-km resolution and an improved representation of mesoscale convective systems to assess future changes in daily extreme precipitation and its drivers. Our high-resolution simulations more realistically capture the observed spatial distribution and intensity of daily extreme precipitation over the historical period than the 100-km resolution counterparts. In a future scenario with high carbon dioxide emissions, daily extreme precipitation over land could increase by about 41% by 2100, mainly as a result of increased mesoscale moisture convergence. The impact of this dynamical contribution to extreme precipitation is underestimated by a factor of three in the low-resolution model. These results highlight the crucial role of high-resolution climate modelling in constraining future extremes and informing more effective climate risk assessments and adaptation strategies.

DOI: 10.1038/s41561-025-01859-1

Source: https://www.nature.com/articles/s41561-025-01859-1