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Abstract. Global climate models (GCMs) have advanced in many ways ascomputing power has allowed more complexity and finer resolutions. As GCMsreach storm-resolving scales, they need to be able to produce realisticprecipitation intensity, duration, and frequency at fine scales withconsideration of scale-aware parameterization. This study uses astate-of-the-art storm-resolving GCM with a nonhydrostatic dynamical core – theModel for Prediction Across Scales (MPAS), incorporated in the atmosphericcomponent (Community Atmosphere Model, CAM) of the open-source CommunityEarth System Model (CESM), within the System for Integrated Modeling of theAtmosphere (SIMA) framework (referred to as SIMA-MPAS). At uniform coarse (here, at 120 km) gridresolution, the SIMA-MPAS configuration is comparable to the standardhydrostatic CESM (with a finite-volume (FV) dynamical core) with reasonableenergy and mass conservation on climatological timescales. With thecomparable energy and mass balance performance between CAM-FV (workhorse dynamical core) and SIMA-MPAS (newly developed dynamical core), it gives confidence inSIMA-MPAS's applications at a finer resolution. To evaluate this, we focuson how the SIMA-MPAS model performs when reaching a storm-resolving scale at3 km. To do this efficiently, we compose a case study using a SIMA-MPASvariable-resolution configuration with a refined mesh of 3 km covering thewestern USA and 60 km over the rest of the globe. We evaluated the modelperformance using satellite and station-based gridded observations withcomparison to a traditional regional climate model (WRF, the WeatherResearch and Forecasting model). Our results show realistic representationsof precipitation over the refined complex terrains temporally and spatially.Along with much improved near-surface temperature, realistic topography, andland–air interactions, we also demonstrate significantly enhanced snowpackdistributions. This work illustrates that the global SIMA-MPAS atstorm-resolving resolution can produce much more realistic regional climatevariability, fine-scale features, and extremes to advance both climate andweather studies. This next-generation storm-resolving model could ultimatelybridge large-scale forcing constraints and better inform climate impactsand weather predictions across scales.