Idealized supercell storms are simulated with two aerosol-aware bulk microphysics schemes (BMSs), the Thompson and the Chen-Liu-Reisner (CLR), using the Weather Research and Forecast (WRF) model. The objective of this study is to investigate the parameterizations of aerosol effects on cloud and precipitation characteristics and assess the necessity of introducing aerosols into a weather prediction model at fine grid resolution. The results show that aerosols play a decisive role in the composition of clouds in terms of the mixing ratios and number concentrations of liquid and ice hydrometeors in an intense supercell storm. The storm consists of a large amount of cloud water and snow in the polluted environment, but a large amount of rainwater and graupel instead in the clean environment. The total precipitation and rain intensity are suppressed in the CLR scheme more than in the Thompson scheme in the first three hours of storm simulations. The critical processes explaining the differences are the auto-conversion rate in the warm-rain process at the beginning of storm intensification and the low-level cooling induced by large ice hydrometeors. The cloud condensation nuclei (CCN) activation and auto-conversion processes of the two schemes exhibit considerable differences, indicating the inherent uncertainty of the parameterized aerosol effects among different BMSs. Beyond the aerosol effects, the fall speed characteristics of graupel in the two schemes play an important role in the storm dynamics and precipitation via low-level cooling. The rapid intensification of storms simulated with the Thompson scheme is attributed to the production of hail-like graupel.