<p>Global climate models (GCMs) have become essential tools for water resources engineers, enabling accurate identification of climatic factors and supporting effective planning and strategic decision-making. The capability to simulate and predict climate scenarios highlights the importance of evaluating GCM performance. Therefore, selecting an appropriate normalization technique in Multi-Criteria Decision-Making (MCDM) is crucial for identifying the ideal GCMs. Ensuring that the normalization technique accurately reflects the variations and significance of different criteria can significantly influence the reliability of the GCM selection process. Using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and India Meteorological Department (IMD) dataset, this investigation analyzes four normalization techniques and five MCDM methods such as Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), Multi-Objective Optimization based on Ratio Analysis (MOORA), Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Compromise Programming (CP). The results identify that vector normalization is the optimal technique for all MCDM methods, except for VIKOR. According to the Consistency Index Ranking (CIR), max-min normalization is the recommended technique for VIKOR. The Spearman’s rank Correlation Coefficient (SCC) test reveals that the consistency of VIKOR differs from that of other MCDM methods, suggesting that VIKOR is not recommended for ranking GCMs. Among the evaluated models, CanESM5 (M9), along with GFDL-ESM4 (M3) and MIROC-ES2L (M18), consistently performed well across multiple normalization techniques and MCDM methods, reflecting their robustness in different decision-making contexts. Overall results lead to an improvement in research quality in selecting GCMs using MCDMs that include the best set of normalization techniques in each MCDM method.</p>