**4. Conclusions**

The results showed that the studied models do not have a high ability to estimate precipitation in the Jean Lesage Intl Station. According to the results of the studied statistics such as the correlation coefficient (R) and Slope, the accuracy of the models was poor and the correlation coefficient in all models was less than 0.5 on a monthly scale. However, in the seasonal scale, the correlation value was reached at 0.75 in the best model. The Slope index was also consistent with the correlation coefficient because in the two investigated models, the distribution of precipitation data was rarely very close to the regression line (1:1) and the Slope value was usually less than 0.5. In addition, the results of the two selected models were close to each other, but the CanESM5 model was more accurate than the other model in the studied Station. The deviation of the projected data and the Station data was very small, which can be shown based on the NRMSE index in all the investigated models as less than 2. In addition, in the selected Station, the Bias index indicated both

7 of 8

models would underestimate the rainfall trend in both time scales. The comparison of the obtained findings showed that the present research results were largely consistent with some other researchers. For example, Hidalgo and Alfaro (2014) showed that most of the CMIP5 models have a low ability to estimate precipitation in the central regions of the United States [11]. Rupp et al. (2013) showed that although the CMIP5 model rainfall data have less accuracy compared to other grided data such as NCEP and ERA40, they estimate the seasonal cycle of precipitation with the same accuracy as networked data in the northwestern regions of America [12]. Mehran et al. (2014) concluded that the CMIP5 model rainfall data are consistent with GPCP data in most parts of the world but do not perform well in dry areas [13]. Ebtehaj and Bonakdari (2023) concluded that the results of the comparison of the CanESM5 and CanESM2 models strongly depend on the month and season, and that the results of CanESM5 are slightly better compared to the other model [5].

Finally, the precipitation trend analysis results for the CanESM5 model and under the two scenarios 4.5 and 8.5 showed that the trend of precipitation changes at the Jean Lesage Intl Station will not be significant. In addition, in scenario 4.5, the precipitation trend decreased in almost half of the year, while in scenario 8.5, the intensity of the decrease and the number of months with a decreasing trend of precipitation were significantly reduced.

**Author Contributions:** Conceptualization, H.B. and T.G.; methodology, H.B.; software, A.S.; validation, A.S. and H.B.; formal analysis, A.S.; investigation, A.S.; resources, H.B.; data curation, H.B.; writing—original draft preparation, A.S.; writing—review and editing, T.G. and H.B.; visualization, A.S.; supervision, T.G. and H.B.; project administration, T.G.; funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project is partially funded by the ESSOR–MARTHE-ET-ROBERT-MÉNARD scholarship administered by CentrEau (Centre québécois de recherche sur la gestion de l'eau) and the Government of Québec Ministère de Sécurité Publique (MSP) under the project 'Compréhension du comportement des rivières en hiver et mesures de gestion des risques liés aux inondations (FLUTEIS; CPS-18-19-26)'.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** GCM datasets are available at Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI:10.24381/cds.c866074c (accessed on 25 November 2022).

**Conflicts of Interest:** The authors declare no conflict of interest.
