资源环境科技发展态势分析平台

Multimodel combining approaches can extract reliable climate information from a large number of climate projections by exploiting the strengths and discounting the weaknesses of each climate simulator; however, most of them (e.g., reliability ensemble averaging [REA]) assign weights to climate simulators without accounting for spatial and temporal variabilities in climate model skills. Here we tested several REAs and proposed a full version that reflects the spatiotemporal (ST) variability of climate model skills. Interperformance evaluations between REA versions showed that, on average, ST-REA reduced the bias by 33.78% and the root mean square error by 11.61%. Therefore, spatial and temporal variabilities in climate model skills can enhance the overall reliability of precipitation projections. ST-REA was applied to project future precipitations over South Korea by combining seven climate models: The spatially averaged projected changes were 2.77%, 8.15%, and 7.58% for the 2020-2039, 2040-2069, and 2070-2099 periods, respectively.