Climate change impact assessment studies often use future projections of only a few global climate models (GCMs) due to limited research resources. Here we develop a novel method to select a small subset of GCMs that widely capture the uncertainty range of large ensemble. By applying this method, we select a subset of five GCM projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble for impact and adaptation studies in Japan. At first, we omit GCMs whose global warming projections have been evaluated to be overestimated in the recent literature. Then, we select a subset of five GCMs that widely captures the uncertainty ranges for 8 climate variables and have good performances in present-climate simulations. These selected GCM simulations will be used to provide better climate scenarios for impact and adaptation studies than those in the previous impact assessment project.

由于研究资源有限，气候变化影响评估研究通常仅使用少数全球气候模型（GCM）的未来预测。在这里，我们开发了一种新颖的方法来选择一小部分GCM，这些GCM广泛捕获了大型整体的不确定性范围。通过应用此方法，我们从耦合模型比较项目第6阶段（CMIP6）集成中选择了五个GCM预测的子集，用于日本的影响和适应研究。首先，我们忽略了全球气候变暖预测已被评估为在最近的文献中被高估的GCM。然后，我们选择五个GCM的子集，这些子集可以广泛捕获8个气候变量的不确定性范围，并且在当前气候模拟中具有良好的性能。