Statistical Characteristics of Environments of Precipitating Mesoscale Convective Systems During the Pre‐Summer Season Over South China

Abstract Mesoscale convective systems (MCSs) are known to induce heavy rainfall and pose significant weather hazards in tropical and mid‐latitude regions. Understanding the environmental conditions of MCSs is essential for improving model simulations and operational precipitation forecasts. In this study, we analyze 929 MCS precipitation events, including 120 events with maximum rain rate exceeding 20 mm/h, over South China from April‒June 2007 to 2020. We also separately investigate 15 warm‐sector heavy rainfall MCS (WMCS) events from the ordinary MCS (OMCS), as they are often mis‐predicted in current weather forecast systems. Our results indicate that moisture and instability are two key factors for MCS precipitation over South China. Water vapor shows a rapid increase 6 hr before the precipitation onset, peaking 1 hr before the maximum precipitation. Under the southwesterly wind, warm and moist air flows from the South China Sea to the coastal regions of South China. The air is lifted to upper levels (at around 450 hPa) due to the strong atmospheric lifting during MCS passage. The WMCS precipitation events exhibit higher moisture conditions and stronger atmospheric lifting than those of the OMCSs. Furthermore, we find that the favorable environment for MCS (e.g., lifting and convergence) is disrupted by mountains when the systems move across mountainous terrain. Consequently, the relationship between precipitation and thermodynamics is weaker for the leeside area of mountains compared with the windward area.

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Journal
Journal of Geophysical Research Atmospheres
Published
2026-06-18
DOI
https://doi.org/10.1029/2025jd045138
Primary Topic
Meteorological Phenomena and Simulations
Type
article
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article

Statistical Characteristics of Environments of Precipitating Mesoscale Convective Systems During the Pre‐Summer Season Over South China

Pak Wai Chan, Hui Su, Shiwei Yu, Yangzhao Gong et al.
Journal of Geophysical Research Atmospheres
Meteorological Phenomena and Simulations
article

Statistical Characteristics of Environments of Precipitating Mesoscale Convective Systems During the Pre‐Summer Season Over South China

Pak Wai Chan, Hui Su, Shiwei Yu, Yangzhao Gong, Naigeng Wu, Cen Wang
article en

Abstract

Abstract Mesoscale convective systems (MCSs) are known to induce heavy rainfall and pose significant weather hazards in tropical and mid‐latitude regions. Understanding the environmental conditions of MCSs is essential for improving model simulations and operational precipitation forecasts. In this study, we analyze 929 MCS precipitation events, including 120 events with maximum rain rate exceeding 20 mm/h, over South China from April‒June 2007 to 2020. We also separately investigate 15 warm‐sector heavy rainfall MCS (WMCS) events from the ordinary MCS (OMCS), as they are often mis‐predicted in current weather forecast systems. Our results indicate that moisture and instability are two key factors for MCS precipitation over South China. Water vapor shows a rapid increase 6 hr before the precipitation onset, peaking 1 hr before the maximum precipitation. Under the southwesterly wind, warm and moist air flows from the South China Sea to the coastal regions of South China. The air is lifted to upper levels (at around 450 hPa) due to the strong atmospheric lifting during MCS passage. The WMCS precipitation events exhibit higher moisture conditions and stronger atmospheric lifting than those of the OMCSs. Furthermore, we find that the favorable environment for MCS (e.g., lifting and convergence) is disrupted by mountains when the systems move across mountainous terrain. Consequently, the relationship between precipitation and thermodynamics is weaker for the leeside area of mountains compared with the windward area.

Journal of Geophysical Research AtmospheresVol. 131(12)
Hong Kong University of Science and Technology (HK), Hong Kong Observatory (CN), University of Hong Kong (HK)
Life below water
Openalex Percentile: Top 6%
Meteorological Phenomena and Simulations
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