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Aerosols are important modulators of the precipitation-generating process, with their concentrations potentially affecting the precipitation process in extreme events. Existing literature suggests that, through microphysical processes, additional aerosols lead to a larger number of smaller cloud droplets, which eventually redistributes the latent heat and the precipitation process. This research addresses the question of how sensitive the spatial and temporal patterns of heavy precipitation events are to aerosol concentration. National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) final (FNL) data were used as input to the Weather Research and Forecasting (WRF) model, to simulate the case study of the catastrophic 2016 flood in Louisiana, USA, for three aerosol loading scenarios: virtually clean, average, and very dirty, corresponding to 0.1×, 1×, and 10× the climatological aerosol concentration. Overall, for the extreme precipitation event in Baton Rouge, Louisiana, in August 2016, increasing aerosol concentrations were associated with 1) a shifted peak precipitation period; 2) a more intense and extreme precipitation event in a more confined area; 3) greater maximum precipitation. Results are important in improving forecast models of extreme precipitation events, thereby further protecting life and property, and more comprehensively understanding the role of aerosols in heavy precipitation events.