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Understanding the impacts of climate change in agriculture is important to ensure optimal and continuous crop production. The agricultural sector plays a significant role in the economy of Upper Midwestern states in the USA, especially that of North Dakota (ND). Spring wheat contributes most of the wheat production in ND, which is a major producer of wheat in the USA. This study focuses on assessing possible impacts of three climate variables on spring wheat yield in ND by building a regression model. Eighty-five years of field data were collected and the trend of average minimum temperature along with average maximum temperature, average precipitation, and spring wheat yield was analyzed using Mann-Kendall test. The study area was divided into 9 divisions based on physical locations. The minimum temperature plays an important role in the region as it impacts the physiological development of the crops. Increasing trend was noticed for 6 divisions for average minimum temperature and average precipitation during growing season. Northeast and Southeast division showed the strongest increasing trend for average minimum temperature and average precipitation, respectively. East-central division had the most decreasing trend for average maximum temperature. A significant relationship was established between spring wheat yield and climatic parameters as the p-value is lower than 0.05 level which rejects the null hypothesis. The regression model was tested for forecasting accuracy. The percentage deviation of error for the model is approximately +/- 30% in most of the years.