This study aims to conduct a thorough investigation to compare the abilities of quantile mapping (QM) techniques as a bias correction method for the raw outputs from general circulation model (GCM)/regional climate model (RCM) combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the coordinated regional climate downscaling experiment (CORDEX) dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.

这项研究旨在进行彻底调查，以比较分位数映射（QM）技术作为一般循环模型（GCM）/区域气候模型（RCM）组合的原始输出的偏差校正方法的能力。由于伊朗的卡尔赫河流域地形特征多样，因此被选为案例研究，以测试在不同条件下的偏差校正方法的性能。两种GCM / RCM组合（ICHEC和NOAA-ESM）的输出是从本研究的协调区域气候降尺度实验（CORDEX）数据集中获得的。结果表明，QM的性能有所不同，具体取决于转换函数，参数集和地形条件。在某些情况下，质量管理人员的调整甚至使GCM / RCM组合的原始产出更糟。这项研究的结果表明，除了DIST，PTF：scale和SSPLIN外，其余考虑的QM方法都可以为降雨和温度变量提供相对改善的结果。应该注意的是，根据从各子流域的不同地形条件获得的结果，经验分位数（QUANT）和鲁棒经验分位数（RQUANT）方法被证明是校正降雨数据偏差的极佳选择，而除了执行的PTF：scale和SSPLIN以外，所有偏差校正方法在温度变量方面的表现都相对较好。