This study addresses the effect of uncertainty in temperature and precipitation inputs and spatial resolution on crop simulation results for Hungary and Romania. Crop yield and harvested area for maize and winter-wheat were simulated using the improved Global Agro-Ecological Zones model (iGAEZ) for the years 1990–1999 with two climate inputs (Climatic Research Units Global 0.5°C Monthly Time Series, Version 2.1 (CRU TS 2.1) and Meteorological Research Institute Global Climate Model with the 20-km mesh horizontal resolution (MRI-GCM20)). The mean, standard deviation and RMSE of the differences between constraint-free and moisture-limited crop yield demonstrate that uncertainty in temperature and precipitation is a significant cause of the considerable uncertainty on crop simulation results at 0.5-degree grid. This uncertainty decreases when simulation results are spatially aggregated to the country scale. Next, to assess the effects of spatial averaging of climate input data, we performed the crop simulations at 0.5 and 0.25-degree grid using MRI-GCM20. The results showed that the correlation of the simulation results at 0.5-degree and at 0.25-degree grid scale is very weak. It seems that within-grid variability in climatic data significantly affects the crop simulation results. Moreover, the comparison of the Food and Agriculture Organization (FAO) yield statistics with crop simulation results shows that simulation results at 0.25-degree grid are much better than that of 0.5-degree grid.