How does Mei-yu precipitation respond to climate change?

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.

Abstract

Under global warming, the thermodynamic and dynamic effects led to increasing trends in intensity of rainfall events, frequency/intensity of extreme precipitation events, and number of days without rainfall in the Yangtze River valley during the Mei-yu period over the past six decades. Model projections suggest that these trends in Mei-yu will continue as global warming intensifies in the future with a relatively large uncertainty.