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The selection of global climate models (GCMs) for a region remained a difficult step in climate change studies. A state-of-the-art Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm is proposed in this paper for GCM selection. The ranking of GCMs obtained using SVM-RFE was compared to that obtained using entropy-based similarity assessment index known as Symmetrical Uncertainty (SU). The study was conducted in the sub-Himalayan region of Pakistan where a reliable projection of climate is highly significant for water resources management in the entire western part of South Asia. The RF-based regression model was employed to generate a multi-model ensemble (MME) mean of the top-ranked GCMs. The MME mean projection was utilized to estimate the spatiotemporal changes in annual precipitation in comparison with precipitation of 1961‐–2000 for various representative concentration pathway (RCP) scenarios. The SVM-RF selected five GCMs (MIROC5, EC-EARTH, CNRM-CM5, BCC-CSM1.1(m) and BCC-CSM1.1) as most suitable for climate change projections in the study area. Obtained results were found to collaborate well with the results of multiple conventional statistical metrics. The MME mean projections revealed precipitation alteration between −1% and 18% during 2020‐–2059, and 0 and 24% during 2060–2099 for different RCPs. Precipitation was projected to increase up to 20% in the north whereas a decrease up-to −16% in the south. • SVM-RFE algorithm is used for selection of GCMs for Upper Indus Basin (IUB). • Performance of SVM-RF was compared with symmetrical uncertainty (SU) and conventional statistics based methods. • SVM-RFE was found to select the same GCMs as selected by SU and conventional statistics. • Precipitation projections in IUB revealed high heterogeneity in future changes in precipitation. [ABSTRACT FROM AUTHOR]