How Well Do Current Global KM-Scale Models Simulate Storms in East Asia’s 2020 Record-breaking Wet Summer

High-performance computing now enables a new generation of global kilometer-scale models. As part of the World Climate Research Programme (WCRP) Global Hackathon 2025 initiative, for the first time, multiple cutting-edge global kilometer-scale models have been run for an entire year. All of them have covered the summer of 2020, when East Asia experienced record-breaking precipitation and catastrophic floods, mainly driven by mesoscale convective systems (MCSs). Using an updated storm-tracking algorithm, this study investigated the performance of six global kilometer-scale models in simulating MCS characteristics during the record-breaking wet summer of 2020 in East Asia. Results revealed that all models generally reproduced MCS characteristics, including MCS size, duration, and key features of convection and precipitation. Models also generally captured finer characteristics such as diurnal variations and the frequency-intensity distribution of hourly precipitation. Among the models, Integrated Forecast System (IFS) performs best in capturing MCS rainfall spatial distribution, Nonhydrostatic ICosahedral Atmospheric Model (NICAM) excels in simulating MCS size, and Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) most accurately represents the land-sea contrast in MCS precipitation intensity. A common bias across models is the underestimation of rainfall area and overestimation of heavy precipitation intensity, indicating simulated convective cores are stronger than observed. Our results demonstrate that global kilometer‑scale modeling has reached a significant benchmark, yet persistent biases remain in MCS simulation. Continued improvements in these models will not only enhance the reliability of modeling but also to improve disaster risk reduction and climate change adaptation.