Climate change affects many meteorological parameters which could result in spatiotemporal variations of the hydrological cycle. These variations can affect local rainfall intensities or design storms and therefore, it is necessary to assess the local effects of climate change in different areas. To do so, meteorological variables under the influence of greenhouse gas changes and socio-economic scenarios must first be simulated. The most reliable method to perform such simulations, is the use of data from the General Circulation Models (GCM). In addition, due to the critical role of precipitation and the significant temporal and spatial variability of this variable, it is imperative to know and accurately study the models that best predict precipitation. Therefore, the current research aims at evaluating the accuracy of the daily precipitation data of the most recent Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6 models), over a historical period from 1953 to 2010, as well as the predicted data for the future between 2010 and 2050 for the Quebec City rain gauge station (Jean Lesage Intl). In this regard, precipitation data were analyzed using statistical Index such as R, NRMSE, BIAS, NARMSE, SLOPE, and RMSRE to find the most accurate model for the study area. The results of this evaluation can help the decision makers to choose the best precipitation model, which is moradaptable to the study area. In addition, the trend of future precipitation will be investigated based on selected models’ scenarios using the Mann-Kendall test.