Yilmaz, MuhammetAlemdar, Kadir DilerTosunoglu, Fatih2026-03-262026-03-2620250177-798X1434-448310.1007/s00704-025-05805-42-s2.0-105018866584https://doi.org/10.1007/s00704-025-05805-4https://hdl.handle.net/20.500.14901/2823General Circulation Models (GCMs) are effective tools for understanding climate change and enabling possible future projections. Although there has been an increase in studies on GCMs assessment, there continues to be a lack of research determining the optimal number of GCMs to be included in a Best Multi-Model Ensemble (BMME). To address this gap, this study integrated the Multi-criteria decision-making methods (MCDM), and the K-means clustering Elbow Method. Firstly, the performance of 27 NEX-GDDP-CMIP6 models was assessed for the period of 1960-2014 with statistical metrics, including the correlation coefficient (CC), percentage bias (PBias), normalized root mean square error (nRMSE), Kling-Gupta efficiency (KGE), and mean absolute error (MAE), taking the fifth generation of European ReAnalysis Land Component (ERA5-Land) as reference. The results showed that NEX-GDDP-CMIP6 generally performs well but fails to capture precipitation characteristics in the northern parts of the country. Individual ranking for each basin was performed using the PROMETHEE method based on performance metric data. PROMETHEE results, in general, the MPI-ESM1-2-LR, MIROC-ES2L, INM-CM5-0, and ECEarth3- Veg-LR models provided sufficient performance in simulating the characteristics of precipitation. General assessment results derived from the integrated method that BMME is the best-performing model across all basins, consistently outperforming both individual NEX-GDDP-CMIP6 and Multi-Model Ensemble (MME) obtained by calculating the simple arithmetic mean of the 27 NEX-GDDP-CMIP6 models. Consequently, the proposed novel methodology can provide valuable information for climate change studies in the study area.eninfo:eu-repo/semantics/closedAccessNex-Gddp-Cmip6PrecipitationMulti-Model EnsembleMulti-Criteria Decision-MakingT & UumlTürkiyeArticle