[EN] Knowledge on the effects of climate change in a system can contribute to the better management of its water and energy resources. This study evaluates the consequences of alterations in the rainfall and temperature patterns for a hydroelectric plant. The methodology adopted consists of four steps. First, a hydrological model is developed for the chosen basin following a semi-distributed and conceptual approach. The hydrological model is calibrated utilizing the optimization algorithm Shuffled Complex Evolution University of Arizona (SCE-UA) and then validated. Secondly, a hydropower model is developed fora hydroelectric plant of the chosen basin. The hydropower model is adjusted to the physical characteristics of the plant. Thirdly, future climate scenarios are extracted from the literature for the studied area. These scenarios include quantitative and seasonal climate variations, as well as different initial reservoir levels. Fourth, the hydrological-hydropower model is simulated for 52 scenarios and the impact of changes in the rainfall and temperature patterns for hydropower generation is evaluated. For each scenario, the water storage in the reservoir and energy produced by the plant are analyzed. The financial impact for extreme scenarios is presented. The methodology is applied to the Tres Marias hydroelectric plant at the upper SAo Francisco river basin (Brazil) and it can be replicated to any other hydropower system. The results show that extreme positive values predicted for rainfall will likely not cause issues to the plant, considering a moderate rise in temperature. However, negative predictions for rainfall, regardless of changes in temperature, should be an alert to the authorities responsible for water and energy resources management. This study was funded by the Sao Paulo Research Foundation (FAPESP -grant #2018-00016-8), European Commission (EBW+ program), and National Council for Scientific and Technological Development (CNPq). The authors thank Companhia Energetica de Minas Gerais S.A. (CEMIG), Agencia Nacional de Aguas (ANA), Instituto Nacional de Meteorologia (INMET), and Camara de Comercializacao de Energia Eletrica (CCEE) for kindly providing the data needed to carry out this research. The authors also thank the developers of RS Minerve, computational tool utilized in this research, and Espaco da Escrita -Pro-Reitoria de Pesquisa (PRP/UNICAMP), for the language services provided.