The performance of a variable-resolution global model, based on the Model for Prediction Across Scales-Atmosphere (MPAS-A) framework and with customized 160-to-1 km resolution grid mesh, was tested by simulating idealized flow fields as well as forecasting the evolution of actual weather systems. The mesh contains five levels of refinement, with 20, 15, 9, 3, and 1 km resolution covering central to East Asia, central to southern China, southeastern China, and Greater Bay Area/Hong Kong, respectively. Using a shallow-water solver and MPAS-A's solver, the mesh was evaluated against standard circularly refined meshes in simulating idealized steady-state flows. Conservation properties and error growths in the 160-to-1 km and counterpart simulations were fairly comparable. By perturbing the steady flow, realistic baroclinic wave evolutions could be captured. Initialized by Global Forecast System (GFS), parallel experiments were further conducted with new-Tiedtke (nTDK), Kain-Fritsch (KF), and Tiedtke (TDK) cumulus schemes and a convection-permitting suite (CP). Experiments showed that the model can give reasonable 5-day outlooks and evolution of synoptic-scale weather systems typically found in East Asia in various seasons. In particular, it can reproduce the mesoscale precipitation related to the Meiyu rainband (cold fronts) in summer (winter). When compared with station data, promising skills in predicting local temperature, humidity, and wind changes were found. It also performed slightly better using nTDK, KF, and TDK schemes, than adopting CP. Overall, by capturing multiscale features concurrently, these experiments gave reasonable global, regional, and local weather predictions, thereby demonstrating the practicality of using customized variable-resolution meshes for high-resolution short-range weather forecasts under MPAS-A framework. Plain Language SummaryWeather forecasting is usually carried out by dividing the atmosphere into many polygonal grid cells and solving physical equations in each cell. In general, the smaller the grid size, the more accurate is the prediction. However, computation becomes extremely expensive if the whole globe is covered by equally small grid cells. A numerical model, designed to accurately predict regional weather patterns, has been set up by using small grids over a target region, but with larger grids elsewhere, so as to minimize the computational cost. It was shown that such model can capture the general behavior of the atmosphere circulation. The model's skill was further assessed, by conducting retrospective predictions of historical weather events over East Asia. The model was capable of capturing the evolution of weather from the global to local scale, thereby demonstrating the practicality of using this model for fine-resolution short-range weather forecasts. Furthermore, it was also found that certain model configurations can give best performance in forecasting East Asian weather in various seasons.