Damage due to climate change is increasing worldwide; in South Korea, the increase in temperature is greater than the average global temperature increase. These changes in temperature have increased the frequency of and damage caused by droughts. To reduce drought damage, the importance of efficient water management policies and evapotranspiration, an index used for water management policies, is increasing. Analyzing the variation in evapotranspiration is relevant to understanding climate change and agricultural water management. Owing to the lack of evapotranspiration data collected using a Lysimeter, evapotranspiration has been estimated using the FAO-56 Penman–Monteith (PM) equation on meteorological datasets as recommended by the United Nations Food and Agriculture Organization. Long-term meteorological data with a maximum of 100 years were collected from 12 sites to estimate evapotranspiration. The objectives of this study were the following: (1) estimate evapotranspiration based on the PM equation, (2) analyze the trends in temperature and evapotranspiration, and (3) evaluate the relationship between temperature and evapotranspiration through correlational analysis. The results improve our understanding of climate change and provide a valuable reference for regional water resource management. We estimated evapotranspiration and analyzed the tendency of temperature and evapotranspiration. As a result, analysis of seasonal ET0 at all stations represented generally increasing trends in spring, summer, and autumn with generally decreasing trends in winter. Results of the seasonal Mann–Kendall test between temperature metrics (maximum, average, minimum) and ET0 showed that the maximum temperature exhibited a distinct increase in spring and winter in some areas. In this study, we determined the strength of the relationship between temperature and ET0 using the Pearson correlation coefficient. The results of evaluating the relationship between each temperature metric and evapotranspiration showed that the maximum temperature had the strongest relationship compared to the average and minimum temperatures.