How does Mei-yu precipitation respond to climate change?
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Updated: Jul 11, 2025

Simulating Impacts of Ice Storms on Forest Ecosystems
Published on: June 30, 2020
Bo Sun1,2,3, Rufan Xue1, Wanling Li1
1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disasters, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Abstract:
Mei-yu is an important weather phenomenon in the middle-lower Yangtze River valley (YRV) region. This study investigates the changes in the characteristics of Mei-yu under global warming and the potential reasons based on observation and reanalysis data during 1961-2022. Notable increasing long-term trends are detected in the number of days without rainfall (NDWOR), the intensity of rainfall events, and the frequency and intensity of extreme precipitation events (EPEs) in the YRV region during the Mei-yu period (15 June-10 July) over past decades. The increasing trend in NDWOR is attributed to decreased relative humidity over land surface and a longer time for the air to be replenished with moisture after rainfall events in a warming climate. The increasing trends in the intensity of rainfall events and frequency/intensity of EPEs are attributed to the strengthened transient water vapor convergence and convection in the atmosphere under global warming. Furthermore, the response of Mei-yu to 2°C of global warming with respect to the pre-industrial climate is analysed using CMIP6 models. The results suggest that the NDWOR, intensity of rainfall events and frequency of EPEs will increase in the YRV region during the Mei-yu period under the 2°C warming scenario, which implies a more challenging climate risk management in the future. Overall, the intensity of rainfall events during the Mei-yu period has the most significant response to climate change in observations and projections. The model results have a relatively large uncertainty.
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