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A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield. However, it can hardly include all relevant factors that affect the yield, and usually overestimates the crop yield when extreme weather conditions occur. In this study, the authors first introduced a drought index (the Standardized Precipitation Evapotranspiration Index) into a process-based crop model (the Agro-C model). Then, the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China, by comparing the model simulations to the statistical records. The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events, compared with its original version. It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.