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A majority of existing studies on uncertainties in climate change impact assessments carried out the uncertainty analysis independently at each stage without quantifying the total uncertainty and thus it was seldom possible to assess the relative contribution of each stage to the total uncertainty and also to see how the uncertainty is propagated as the stage proceeds. To overcome these shortcomings, this study proposes a simple yet new approach, which can quantify the total uncertainty as well as the incremental uncertainty at each stage. Employing the maximum entropy as an uncertainty measure, the new approach was applied to a case study that consists of two emission scenarios, four global climate model GCM scenarios, two downscaling techniques, and two hydrological models. The difference was noteworthy: in case of the water streamflow projection, the conventional approach identified the GCM stage as the largest contributor (89.34%) to the total uncertainty while this new approach concluded the emission scenario stage the largest (58.66%). In case of the precipitation projection, the downscaling stage produced the largest uncertainty indicating that the relative uncertainty contribution of each assessment stage can vary depending on the projection variable which of uncertainty is examined. The case study also compared the projection uncertainty with the natural variability that exists in the observed data and concluded that the uncertainty generated by the future climate change projection is about two times larger than that of the past natural variability.

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.

Going by the research results of the past, of all the uncertainties resulting from the

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