Scientists occasionally predict projected changes in extreme climate using multi-model ensemble methods that combine predictions from individual simulation models. To predict future changes in precipitation extremes in the Korean peninsula, we examined the observed data and 21 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over East Asia. We applied generalized extreme value distribution (GEVD) to a series of annual maximum daily precipitation (AMP1) data. Multivariate bias-corrected simulation data under three shared socioeconomic pathway (SSP) scenarios—namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5—were used. We employed a model weighting method that accounts for both performance and independence (PI-weighting). In calculating the PI-weights, two shape parameters should be determined, but usually, a perfect model test method requires a considerable amount of computing time. To address this problem, we suggest simple ways for selecting two shape parameters based on the chi-square statistic and entropy. Variance decomposition was applied to quantify the uncertainty of projecting the future AMP1. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1973–2010), were estimated for three overlapping periods in the future, namely, period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From these analyses, we estimated that the relative increases in the observations for the spatial median 20-year return level will be approximately 18.4% in the SSP2-4.5, 25.9% in the SSP3-7.0, and 41.7% in the SSP5-8.5 scenarios, respectively, by the end of the 21st century. We predict that severe rainfall will be more prominent in the southern and central parts of the Korean peninsula.

中文翻译：

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带有统计框架的CMIP6集合对朝鲜半岛极端降水的未来预测和不确定性评估

科学家偶尔会使用多模型集合方法来预测极端气候下的预计变化，这些方法结合了来自各个模拟模型的预测。为了预测朝鲜半岛极端降雨的未来变化，我们检查了东亚地区耦合模型比较项目第六阶段（CMIP6）的观测数据和21个模型。我们将广义极值分布（GEVD）应用于一系列年度最大日降水量（AMP1）数据。使用了在三个共享的社会经济途径（SSP）情景下（即SSP2-4.5，SSP3-7.0和SSP5-8.5）的多变量偏差校正的模拟数据。我们采用了模型加权方法来说明性能和独立性（PI加权）。在计算PI权重时，应确定两个形状参数，但通常，完美的模型测试方法需要大量的计算时间。为解决此问题，我们建议基于卡方统计量和熵来选择两个形状参数的简单方法。应用方差分解来量化预测未来AMP1的不确定性。估计未来20年和50年的回报水平以及相对于参考年（1973-2010年）的回报期为三个重叠的时期，即第一期（2021-2050年），第二期（2046年） –2075）和期间3（2071-2100）。从这些分析中，我们估计，空间中位数20年回报水平的观测值的相对增加在SSP2-4.5中约为18.4％，在SSP3-7.0中为25.9％，在SSP5-8.5中为41.7％。分别在21世纪末。