The study utilizes the Weather Research and Forecasting (WRF) model to simulate the heavy rainfall event (HRE) over the Gujarat in 2022. A multiphysics ensemble modeling framework is used to elucidate the new scientific insights causing this event up to a forecast lead time of 96 hr. The ensembles comprise six schemes for planetary boundary layer, cloud microphysics, cumulus, and two land‐use and land surface parameterizations (a total of 44 experiments). Based on the comprehensive rating metrics (CRM), the top three (i.e., E6, E2, EISRO) and bottom three (i.e., EUSGS, E8, E7) ensemble models are segregated. Results indicate that among all the CRM, total precipitation (PRCPTOT) and simple daily intensity index (SDII) contribute majorly toward the ranking of a model. In general, the ensemble has superior performance compared to deterministic models. Dynamical parameters (i.e., vertically integrated moisture flux transport [VIMFT], low‐level convergence, wind, and relative vorticity at 850 hPa) in all the ensembles captured the HRE signature robustly on day 1 and day 2 compared to day 3 and day 4. However, the mixed‐parameterized model (EISRO) produced the most realistic structure, emphasizing the importance of LULC. It is revealed that, during the initial hours (up to day 2) of the HRE, dynamical processes mainly regulate the convective parameters and as the system matures thermodynamical processes are dominant with the least contribution from transient eddy processes. Further analysis demonstrated that dynamical, thermodynamical, and transient eddy processes show unique patterns regulating the moisture distribution over the region. Beside the distinct north–south alignment of localized convection, the dynamical processes are noted to have the strongest correlation with the VIMFT followed by thermodynamical and eddy processes over the impacted region.
