Impact of Cumulus Parameterization and Cloud Microphysics on WRF Simulation of an Extreme Rainfall Event over Maharashtra, India: A 3 by 3 Factorial Assessment

Simulating extreme monsoon rainfall over the Konkan coast and Western Ghats remains challenging at 9 km resolution, where convection is partly resolved and partly parameterised. This study evaluates the sensitivity of Weather Research and Forecasting (WRF) simulations of the 5-7 July 2026 Maharashtra rainfall event to cumulus and cloud microphysics schemes. Nine simulations employed a 3 x 3 factorial design combining WSM6, WDM6 and Thompson microphysics with Kain-Fritsch, Tiedtke and Grell-Freitas cumulus schemes. Rainfall was evaluated against IMD 0.25-degree gridded observations, with IMERG and ERA5 as additional references. A descriptive factorial decomposition showed that cumulus parameterisation accounted for 97.3% of across-experiment variation in mean rainfall, 88.2% in RMSE and 84.7% in spatial correlation, while microphysics contributed 2.0%, 2.1% and 9.3%, respectively. Kain-Fritsch produced the highest spatial correlations (0.533-0.581) but substantially overestimated rainfall. Tiedtke achieved the smallest mean biases and RMSE, although compensating regional errors concealed a 28-34% rainfall deficit over the Konkan-Ghats belt and substantial underestimation on 7 July. Grell-Freitas showed large wet biases and relatively high RMSE. All nine simulations displaced the event-total rainfall maximum approximately 140-195 km northward. WSM6-Tiedtke achieved the lowest RMSE, whereas Thompson-Kain-Fritsch better represented the rainfall spatial pattern. The findings demonstrate that, for this event and fixed model configuration, cumulus parameterisation exerted substantially greater influence on simulated rainfall than cloud microphysics. However, a single-event, single-member experimental design does not establish a universally optimal physics configuration.