Maximum numerical weather prediction models have their own inherent biases and these biases have high impact on accuracy of weather forecast. Hence, bias correction is an essential part of any study for any model output datasets. The current study uses a weather research and forecasting (WRF) model, simulated daily precipitation of winter season (December to February: DJF) for the period of 2010–2011 to 2016–2017 (7 years) for the bias correction and validated against observed precipitation of Snow and Avalanche Study Establishment (SASE), India. For the first time, three different methods, i.e., empirical quantile mapping (QM), linear scaling (LS), and regression (REG) have been studied for the bias correction over the Northwest Himalaya region. In order to identify the best method out of these three, four statistical measurements, i.e., skill score (SS) and its decompositions, bias in percentage, root mean square errors (RMSE), and percentile values have been examined. Based on the analysis of SS and RMSE, it is worth to note that the QM method is found to be most suitable method for the December and February forecast of WRF model, whereas the LS approach is most suitable for the January forecast. Comparison based on Taylor’s diagram and percentiles via boxplot shows that the quantile mapping approach is most advisable for bias correction to the model simulated precipitation dataset over Northwest Himalaya region.

最大数值天气预报模型具有其自身的固有偏差，这些偏差对天气预报的准确性有很大影响。因此，对于任何模型输出数据集，偏差校正都是任何研究的重要组成部分。本研究使用天气研究和预报（WRF）模型，模拟了2010-2011年至2016-2017年（7年）的冬季（12月至2月：DJF）的日降水量，用于偏差校正并根据观测值进行了验证。印度Snow and Avalanche研究机构（SASE）的降水。第一次，为喜马拉雅西北地区的偏差校正研究了三种不同的方法，即经验分位数映射（QM），线性缩放（LS）和回归（REG）。为了从这三个，四个统计量中找出最好的方法，即 技能得分（SS）及其分解，百分比偏差，均方根误差（RMSE）和百分位值已得到检查。基于SS和RMSE的分析，值得注意的是，发现QM方法是WRF模型的12月和2月预测的最合适方法，而LS方法最适合1月的预测。通过基于箱图的泰勒图和百分位数进行的比较表明，分位数映射方法最适合于对喜马拉雅西北地区的模拟降水数据集进行偏差校正。值得注意的是，发现QM方法最适合WRF模型的12月和2月预测，而LS方法最适合1月预测。通过基于箱图的泰勒图和百分位数进行的比较表明，分位数映射方法最适合于对喜马拉雅西北地区的模拟降水数据集进行偏差校正。值得注意的是，发现QM方法最适合WRF模型的12月和2月预测，而LS方法最适合1月预测。通过基于箱图的泰勒图和百分位数进行的比较表明，分位数映射方法最适合于对喜马拉雅西北地区的模拟降水数据集进行偏差校正。