Abstract Mesoscale convective system (MCS) is a major contributor to extreme precipitation over East Asia, but their long‐term trends remain insufficiently understood. Here, we assess the capability of the weather research and forecasting model, configured as a convection‐permitting model (CPM) to simulate MCS characteristics and 23‐year trends over East Asian monsoon region from 2001 to 2023 (June–September) by comparing with high‐resolution observational data. We employed an MCS tracking method PyFLEXTRKR, which can identify and track MCSs based on precipitation and brightness temperature. The CPM effectively captures key MCS characteristics, including lifetime, lifecycle total precipitation amount, and movement speed. However, it also has systematic biases: the model underestimates MCS size and meso‐ α MCS frequency while overestimating meso‐ β MCS occurrence and both mean and maximum MCSs precipitation intensities. Despite these biases, the model captures increasing (decreasing) trends in total and MCS precipitation over Manchuria and eastern China (Taiwan). In contrast, it struggles to reproduce observed total and MCS precipitation trends over North China Plain (NCP) and the Korean Peninsula (KP). These biases stem from the model's inability to capture enhanced moisture transport into East Asia, resulting in an underestimation of low‐level moisture over NCP and KP, as indicated by trends in vertically integrated moisture flux and 850 hPa specific humidity. By characterizing systematic and regional model biases, this study lays the groundwork for more reliable CPM‐based assessments of MCS responses to climate variability and change.
Mun et al. (Sun,) studied this question.
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