Climate internal variability (CIV) plays an important role in understanding climate and is one of the principal uncertainties in climate projections. This study aims to estimate CIV and climatological mean (CM) in predictions using different emission scenarios for South Korea. A stochastic weather generator is employed to generate 100 ensembles of 30-year hourly time series for 40 meteorological stations. CIV is then estimated from the detrended method and compared with the noise computed by the two approaches. The extremely high value of the coefficient of determination between CIV values and noise indicates that the methodologies are seamless. The key results of this study include: (1) national average CM and CIV will increase in the future, and that increase will be greater in Representative Concentration Pathway 8.5 and end periods; (2) the nature of future changes in CM and CIV differ according to the indices of interest. Characteristics of three precipitation-quantity indices (total precipitation, totPr; daily maximum precipitation, maxDa; and hourly maximum precipitation, maxHr) and the precipitation-occurrence index (the number of days without precipitation, nonPr) are largely distinct; (3) examining the relationship between factors of changes of CIV and CM reveal a high correlation between them for maxDa and maxHr, but not for other indices; (4) The tail information of distribution for the FOC ratio implies that future changes in total and extreme precipitation are likely to be decoupled for some months or at some locations. The degree of decoupling is more noticeable on the hourly than the daily scale; and (5) the spatial deviation of CIV is also larger during the summer when CIV values are spatially large; this is valid only for totPr and maxDa. Methodologies and results for finer scales help assess the impact of climate change and develop appropriate adaptation and response strategies.

气候内部变率（CIV）在理解气候中起着重要作用，并且是气候预测的主要不确定性之一。这项研究旨在估计韩国使用不同排放情景的预测中的CIV和气候平均数（CM）。随机天气生成器用于为40个气象站生成100个30年每小时时间序列的集合。然后根据去趋势方法估算CIV，并将其与两种方法计算出的噪声进行比较。CIV值与噪声之间的确定系数值极高，表明该方法是无缝的。这项研究的主要结果包括：（1）未来，全国平均CM和CIV将会增加，并且在代表浓度路径8.5和末期，这种增加将更大。（2）CM和CIV未来变化的性质根据关注指标而有所不同。三种降水量指数（总降水量，ttPr；日最大降水量，maxDa；和每小时最大降水量，maxHr）的特征和降水发生指数（无降水量的天数，nonPr）有很大的区别。（3）检查CIV和CM变化因素之间的关系，发现它们之间对于maxDa和maxHr具有高度相关性，而对于其他指标则没有相关性；（4）FOC比率分布的尾部信息表明，未来总降水量和极端降水的变化可能会在几个月或某些位置解耦。每小时的去耦程度比每日的程度更明显；（5）夏季，当CIV值空间较大时，CIV的空间偏差也较大；这仅对totPr和maxDa有效。规模更小的方法和结果有助于评估气候变化的影响，并制定适当的适应和应对策略。