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This study uses the Weather Research and Forecasting (WRF) Model to investigate the performance of hail parameterizations of the WRF double-moment 7-class (WDM7), aerosol-aware Thompson (AAT), and National Taiwan University triple-moment (NTU3M) bulk microphysics schemes (BMSs) on a real case of a hailstorm initiated in Shandong Province, China. The maximum hail size is particularly evaluated because it is crucial to hail severity prediction, along with areal coverage and intensity of the 24-h solid precipitation during the simulations. Compared with the radar-derived maximum hail size, the objective analysis shows that the NTU3M scheme has the best score in the forecast skill of hail-fall coverage and size, while two BMSs with single-moment rimed ice species overestimate hail diameters aloft but underpredict the coverage at the surface. A deeper investigation suggests that the derived size tendencies from the three BMSs are comparable to the benchmark solutions from the detailed hailstone growth and melting models. The NTU3M scheme displays the most consistent size tendency of the maximum diameter with the benchmark solution in the growth processes. The behaviors of melted diameter by parameterizations are highly related to the treatments of number concentration, which are consistent with the predicted hail severity and coverage. Finally, the sensitivity study shows that increasing the model resolution does not improve the forecast of the maximum hail size, given the biases in the hail mass budget equations and the parameterization of particle size distribution, with single-moment rimed ice species of the AAT scheme. Significance Statement: Improving hail-forecasting skill, including the size, severity, and the spatial and temporal coverage of hail fall, has become an important subject for numerical weather prediction models as the model resolution increases. The objective of this study is to investigate the fundamental differences in hail parameterizations of three bulk microphysics schemes that lead to differences in the prediction of severe hail events and the spatial coverage of hail fall, hopefully providing insights into hail prediction with a regional numerical weather prediction model in the future. [ABSTRACT FROM AUTHOR]