Vortex–MCS–Precipitation Linkages: Sensitivity to Cumulus Convection Parameterization in Global Kilometer-Scale Models during China’s Meiyu Season

Global storm-resolving simulations at kilometer scales (1–5 km) provide new opportunities to represent convective processes, yet they remain in the gray zone of deep convection, where cumulus parameterization choices can strongly affect model performance. Using global kilometer-scale simulations from the Digital Earth Global Hackathon 2025, this study applies a new vortex–mesoscale convective system (MCS) tracking and matching algorithm to examine how two convection parameterization configurations—turning off deep convection (IFS-deepoff) and deep convection with reduced cloud-base mass flux (IFS-rcbmf)—influence the simulation of MCSs, vortices, and precipitation during the 2020 Meiyu season over East Asia. Results show that IFS-deepoff outperforms IFS-rcbmf in reproducing the total amount and spatial distribution of precipitation, although both schemes overestimate MCS frequency and their contribution to rainfall over the Sichuan Basin and the middle–lower Yangtze River. Importantly, precipitation biases are not governed by MCS frequency alone, but depend strongly on the coupling between MCSs and vortices. Precipitation in both schemes is highly sensitive to vortex simulation, with IFS-deepoff producing stronger extremes due to enhanced moisture convergence associated with boundary-layer vortices and increased convective available potential energy (CAPE). These findings highlight vortex–MCS coupling as a critical control on precipitation in the kilometer-scale gray zone, demonstrating that convection parameterization influences rainfall primarily through its modulation of multiscale dynamical interactions. This study provides new insight for improving convection treatment in next-generation global storm-resolving models.