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Understanding the response of sulfate to climate change is crucial given tight couplings between sulfate and the hydrological cycle. As the sources and sinks of sulfate are sensitive to cloud and precipitation processes, the accuracy of model simulations depends on the accuracy of these meteorological inputs. In this study, we evaluate the GEOS-Chem model in simulating summertime surface sulfate concentrations in the continental United States across different levels of dryness and compare the model performance based on two sets of meteorological fields: Modern Era Retrospective Analysis for Research and Applications (MERRA) and MERRA-2. Both simulations fail to reproduce observed increases in sulfate during drought, as indicated by negative correlation slopes between surface sulfate concentrations and the standardized precipitation evapotranspiration index (SPEI). This deficiency can be largely attributed to too large a decrease in clouds and hence aqueous phase sulfate production as conditions shift from wet to dry. MERRA-2-driven GEOS-Chem (M2GC) shows improvements in cloud and precipitation fields relative to the MERRA-driven GEOS-Chem, hence eliminating approximately half of the bias in the simulated sulfate-SPEI slope. However, M2GC still underestimates boundary layer cloud fraction, overestimates liquid water content, and overestimates the rates of the decrease in both quantities as conditions become drier. Explicitly correcting these cloud biases in M2GC results in a 60-80% reduction of the bias in the simulated sulfate-SPEI slope. The strong sensitivity of simulated sulfate to prescribed cloud fields suggests the need for more comprehensive assessment of cloud inputs for sulfate simulations under current and future climate change scenarios.