The relationships between crop yields and meteorology are naturally non-stationary because of spatiotemporal heterogeneity. Many studies have examined spatial heterogeneity in the regression model, but only limited research has attempted to account for both spatial autocorrelation and temporal variation. In this article, we develop a novel spatiotemporally varying coefficient (STVC) model to understand non-stationary relationships between crop yields and meteorological variables. We compare the proposed model with variant models specialized for time or spatial, namely spatial varying coefficient (SVC) model and temporal varying coefficient (TVC) model. This study was conducted using the county-level corn yield and meteorological data, including seasonal Growing Degree Days (GDD), Killing Degree Days (KDD), Vapor Pressure Deficit (VPD), and precipitation (PCPN), from 1981 to 2018 in three Corn Belt states, including Illinois, Indiana, and Iowa. Allowing model coefficients varying in both temporal and spatial dimensions gives the best performance of STVC in simulating the corn yield responses toward various meteorological conditions. The STVC reduced the root-mean-square error to 10.64 Bu/Ac (0.72 Mg/ha) from 15.68 Bu/Ac (1.06 Mg/ha) for TVC and 16.48 Bu/Ac (1.11 Mg/ha) for SVC. Meanwhile, the STVC resulted in a higher R² of 0.81 compared to 0.56 for SVC and 0.64 for TVC. The STVC showed better performance in handling spatial dependence of corn production, which tends to cluster estimation residuals when counties are close, with the lowest Moran's I of 0.10. Considering the spatiotemporal non-stationarity, the proposed model significantly improves the power of the meteorological data in explaining the variations of corn yields.