In this study, the effects of bias correction methods on the daily precipitation in South Korea produced by five regional climate models (RCMs), and the impact of bias-corrected precipitation on projected climate change is investigated. We use four bias correction methods (Linear Scaling (LS) Power Transformation (PT), Quantile Mapping for the entire period (QME), and Quantile Mapping for each month (QMM)). We perform pre-processing corrections for the dry period in advance of QME and QMM for the wet period. All bias correction methods improve both long-term temporal and spatial averaged precipitation in the present-day period. However, the second peak of the annual precipitation cycle in the QME method is underestimated. LS shows poor correction skills for the intensity and frequency of extreme precipitation. The pre-processing for the dry period also helps to correct the intensity and frequency of daily precipitation. The corrected precipitation characteristics could vary depending upon the bias correction method. Thus, bias correction methods must be carefully chosen according to the statistical features such as the mean and extreme values that should be corrected. For future analysis, PT and QMM are only applied to improve daily precipitation. RCMs simulate the increase in precipitation mainly in the southern regions of Korea. RCMs also show that the second precipitation peak of the annual cycle is significantly strengthened. The intensity of extreme precipitation is increased significantly in the projection of the two scenarios. Bias correction can contribute to the improvement of precipitation variability retaining the characteristics of raw RCM data.

在这项研究中，研究了偏差校正方法对由五个区域气候模型（RCM）产生的韩国日降水量的影响，以及偏差校正后的降水量对预计的气候变化的影响。我们使用四种偏差校正方法（线性缩放（LS）功率变换（PT），整个期间的分位数映射（QME）和每个月的分位数映射（QMM））。我们在潮湿时段之前对QME和QMM进行干燥时段的预处理校正。所有偏差校正方法都可以改善当今时期的长期时空平均降水。但是，QME方法中年降水周期的第二个峰值被低估了。LS对极端降水的强度和频率显示出较差的校正技巧。干燥期的预处理还有助于纠正日常降水的强度和频率。校正后的降水特性可能会根据偏差校正方法而有所不同。因此，必须根据统计特征（例如应校正的平均值和极值）仔细选择偏差校正方法。为了将来分析，仅将PT和QMM用于改善每日降水量。RCM主要在韩国南部地区模拟降水的增加。RCM还显示，年周期的第二个降水高峰显着增强。在这两种情况的预测中，极端降水的强度显着增加。