Maize is the most widely cultivated crop in the United States. It is grown in fields representing diverse soils as well as continuously changing weather conditions. The diversity of these conditions can produce variation in performance across fields. In this study, multi-environment field trials conducted using an augmented randomized block design were investigated to determine the effects of soil and weather on genotype performance and genotype by environment interaction (GEI).  A number of statistical methods such as principal component regression were used to estimate the GEI and possible causes of this interaction. In preliminary analyses, we observed that certain weather and soil factors during the pre-planting and vegetative phase of crop development had significant influence on hybrid performance and variation in performance across locations. We also extended the results from this analysis through the incorporation of process-based crop models such as ‘Decision Support System for Agrotechnology Transfer’ (DSSAT). These models are being utilized to predict crop performance under various climate scenarios including extreme weather conditions. The information obtained from our models can be used to inform breeders, researchers, and farmers about the magnitude and nature of GEI, causes of interaction, adaptation of a crop, and behavior of a crop under extreme environmental conditions.