The main objective of this study is to develop a general methodology for predicting soil temperature based on general circulation data. To meet this demand, we used temperature data that can be profitably used to predict soil temperature in a period of 20 years. Accordingly, air temperature data were downscaled to 2016–2025 based on LARS-WG data. The obtained results indicated that the model has precisely predicted minimal and maximal temperatures. According to the results, the best correlation methods are S, cubic, and quadratic. To investigate soil temperature changes, the predicted data were classified and categorized into two separate decades (2016–2025 and 2026–2035). The results showed that air temperature increases to 1 WC and 1.2 WC in the first decade (2016–2025) and the second decade (2026–2035), respectively, but varies in different regions. The predicted air temperature is lower in the eastern part of the region. In the central region, air and soil temperatures are predicted to be greater than that of other regions. It should also be mentioned that a variety of temperature changes are related to the depth of soil.
