How Well Do Global Storm-Resolving Models Simulate Precipitation Associated with Co-Occurring Atmospheric Features?

Co-occurring weather systems frequently produce heavy precipitation that affects society and infrastructure, but simulating these events remains challenging because precipitation extremes depend on interacting processes across scales. We examine how five global storm-resolving models (GSRMs) with 2.5-10-km horizontal grid spacing simulate tropical cyclones (TCs), extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), their co-occurrence, and associated total and extreme precipitation. Using feature tracking and a hierarchical co-occurring feature attribution framework, we compare GSRMs with satellite and reanalysis-based observations. Most GSRMs reproduce enhanced ETC and AR frequency over storm-track regions, but MCS and TC frequencies show large intermodel spread. Most GSRMs rank MCSs as the leading tropical total-precipitation contributor and identify MCS-AR-ETC as a leading contributor over extratropical oceanic storm tracks, consistent with observations. For tropical extremes, all GSRMs identify MCSs as the dominant contributor, but contributions range from 43%-85% over land and 37%-64% over ocean, with most models below observed values of 83% and 76%, respectively. Over Northern Hemisphere land, MCS, MCS-ETC, and deep convection together account for about 70% of observed extremes, and three of five GSRMs reproduce the top two contributors. Observed ETCs co-occurring with ARs and MCSs are the strongest and wettest ETCs, and embedded MCSs are larger, longer-lived, faster-moving, and produce 2-3 times as much rain volume as isolated MCSs. GSRMs largely reproduce these relationships, although precipitation intensity and MCS contribution magnitudes vary substantially across models, indicating remaining uncertainty in storm organization and precipitation processes.