Climate change studies usually include the use of many projections, and selecting an essential number of projections is very important, because using all Global Climate Model (GCM) scenarios is impossible in practice. Furthermore, the climate change impact assessment is often sensitive to the choice of GCM scenarios. This study suggests that selecting the best-performing scenarios based on a historical period should be avoided in nonstationary cases like climate change, and then proposes a new approach that can preserve the uncertainty, that all scenarios contain. The new approach groups all GCM scenarios into several clusters, and then selects a single representative scenario among member scenarios of each cluster, based on their skill scores. The proposed approach is termed ‘selecting the principal scenarios’, and applied to select five principal GCM scenarios for the South Korean Peninsula, among 17 GCM scenarios of the 20C3M emission scenario. The uncertainty preservation is measured with the maximum entropy theory. The case study presents that the principal scenarios preserve the full range of total uncertainty, compared to less than 65% for the best scenarios confirming that preserving uncertainty with the principal scenarios is more adequate, than selecting the best-performed scenarios, in climate change studies.