Abstract This study evaluated the performance of various global storm‐resolving models (GSRMs) in simulating the Indian Summer Monsoon (ISM), highlighting the limitations of current global kilometer‐scale models in accurately representing precipitation patterns over the Indian subcontinent. The DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains) project provides a critical intercomparison platform for these models. Participating models‐such as ARPNH, GEOS, MPAS, SAM, NICAM, FV3, and UM‐explicitly resolve deep convection, reducing dependence on parameterizations that often introduce biases in low‐resolution models. Despite operating at high spatial resolutions (ranging from 1.5 to 5 km), these models exhibit notable discrepancies in simulating Indian Summer Monsoon (ISM) precipitation. For instance, NICAM and FV3 tend to overestimate precipitation across longitudes (65–100°E) while the UM model significantly underestimates it over the central Indian region. These variations are largely due to differences in how convection statistics and rainfall probability distributions are represented in the models. Interestingly, these models simulated moderate to heavy rainfall events with comparatively less bias than other categories. However, this is insufficient on its own to deliver accurate kilometer‐scale forecasts. Improvements in numerical schemes and the representation of physical processes, particularly cloud microphysics, are essential for advancing the simulation of ISM rainfall on a seasonal scale, both in mean and distribution.
Lekshmi et al. (2026) studied this question.