Introduction: As a tropical archipelago, Indonesia is exceptionally susceptible to climate change impacts. Since mitigation requires accurate regional climate data, a reliable model assessment is essential to address the biases and uncertainties of Global Climate Model’s (GCMs). This study evaluates and ranks 35 NASA NEX-GDDP-CMIP6 models, including their multi-model mean ensemble (ENSMEAN), on their capacity to simulate monthly precipitation climatology over Indonesia. Methods: The methodology employed MSWEP dataset as the observational reference, utilizing statistical metrics including Correlation Coefficient (CC), Normalized Standard Deviation (NSTD), Root Mean Square Deviation (RMSD) and Mean Bias (MB). A dual-scale evaluation framework was adopted, assessing the model’s spatio-temporal performance. Taylor Diagrams used to visualize model distribution, while Min-Max normalization and the Summation of Rank (SR) applied to ensure fair comparison and identify the best-performing models. Discussion: The findings demonstrate that NEX-GDDP-CMIP6 models generally capture Indonesia’s seasonal precipitation patterns in close alignment with MSWEP observations. Notably, five models that consistently identified as high performers across spatio-temporal dimensions were ACCESS-CM2, CMCC-ESM2, TaiESM1, MRI-ESM2-0 and CESM2-WACCM. Specifically, ACCESS-CM2 showed the highest temporal accuracy, while TaiESM1 demonstrated the strongest spatial accuracy. ENSMEAN ranked seventh across spatio-temporal dimensions, proving its capability of reducing errors and enhancing simulation reliability. Despite the model’s overall accuracy, systematic biases persists, such as a “February dip” that underestimates peak wet-season precipitation and a tendency to overestimate precipitation during the dry season. These discrepancies suggests that simulating precipitation interactions among monsoon dynamics, topography and land-sea contrast remain challenging in Indonesia Maritime Continent. This study offers a benchmark for GCMs selection and underscores the need for improved regional models to support climate adaptation and hydrological policymaking. Copyright © 2026 Prasetya, Djuhana, Saputro and Permana.