Superparameterized (SP) global climate models have been shown to better simulate—as compared to conventional models—various features of precipitation, including diurnal timing as well as extreme events. While various studies have looked at the effect of differing microphysics parameterizations on precipitation within limited-area cloud-resolving models, we examine here the effect on continental-US extremes in a global SP model. We vary the number of predicted moments for hydrometeor distributions, the character of the rimed ice species, and the representation of raindrop self-collection and breakup. Using a likelihood ratio test and accounting for the effects of multiple-hypothesis testing, we find that there are some regional differences, both in the current climate and in a warmer climate with uniformly increased sea-surface temperatures. These differences are most statistically significant and widespread when the number of moments is changed. To determine whether these results are due to (fast) local effects of the different microphysics or the (slower) ensuing feedback on the large-scale atmospheric circulation, we run a series of short, 5-day simulations initialized from reanalysis data. We find that the differences largely disappear in these runs and therefore infer that the different parameterizations impact precipitation extremes indirectly via the large-scale circulation. Finally, we compare the present-day results with hourly rain-gauge data and find that, for the model configuration and resolution used, SP underestimates extremes relative to observations regardless of which microphysics scheme is used.