Sensitivity of East Asian Meso-β-Scale Convective Systems Identification to Microphysical Processes in a Convection-Permitting Model

Mesoscale convective systems (MCSs) are key contributors to heavy rainfall in the East Asian summer monsoon, yet their statistics and internal structure remain difficult to simulate, even in convection-permitting models (CPMs). This study evaluates the performance of the newly developed Unified Forecast System Double-Moment microphysics scheme (UDM) in a convection-permitting configuration of the Weather Research and Forecasting (WRF) model for June-September 2020 over East Asia. We compare UDM and the WRF Double-Moment 7-class microphysics scheme (WDM7) against satellite-based identification of meso-α- and meso-β- scale convective systems (αMCSs and βMCSs), and perform component-wise sensitivity experiments in which individual UDM components (reduced ice-phase accretion rates, warm-rain autoconversion, in-cloud microphysics, and semi-Lagrangian rain sedimentation) are replaced by their WDM7 components. UDM substantially mitigates the systematic overestimation of βMCSs, reducing it from approximately 66% in WDM7 to 6%, while slightly alleviating the underestimation of αMCSs (by ∼11%). Composite analyses show that UDM produces broader, colder cloud shields and richer upper-level ice and snow, along with stronger mid-tropospheric updrafts and higher equivalent potential temperature in convective cores. In-cloud microphysics and ice-phase accretion rates are the dominant contributors to these improvements, whereas warm-rain autoconversion plays a secondary role. They shift hydrometeor partitioning from fast-falling hail toward slower ice and snow, resulting in a positive microphysics-dynamic feedback that sustains extended stratiform anvils. These results highlight the importance of physically consistent microphysics parameterizations for realistic simulation of MCS statistics in CPMs, and UDM provides a physically tested microphysics configuration that can be applied in future climate change experiments.