In this study, a non-local model output statistics (MOS) approach is proposed for the downscaling of daily rainfall of coupled model intercomparison project phase 5 (CMIP5) general circulation models (GCMs) for the projections of rainfall in Peninsular Malaysia for two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5. Projections of eight GCMs for both the mentioned RCPs were used for this purpose. The GCM simulations were downscaled at 19 observed stations distributed over Peninsular Malaysia. Random forest (RF) was used for the development of non-local regression-based MOS models. The results revealed a high accuracy of the models in downscaling rainfall at all the observed stations. The mean absolute error (MAE) of the models were found in the range of 0.8–0.39; normalized root mean square error (NRMSE) between 7.4 and 41.7, Percent Bias (PBIAS) between –0.3 and 10.1, Nash–Sutcliffe coefficient (NSE) between 0.81 and 0.99 and R² between 0.89 and 0.99. The increase in annual rainfall was in the range of 7.3–29.5%. The increase was higher for RCP8.5 compared to RCP4.5. The maximum increase was observed in the northern part of Peninsular Malaysia in the range of 20.7–29.5%, while the minimum in the south-west region was in the range of 7.6–15.2%.

在这项研究中，提出了一种非本地模型输出统计（MOS）方法来降低耦合模型相互比较项目第5阶段（CMIP5）的总环流模型（GCM）的日降水量，以预测马来西亚半岛的两个代表性降雨集中路径（RCP）方案，RCP4.5和RCP8.5。为此，使用了两个上述RCP的八个GCM的投影。GCM模拟在分布于马来西亚半岛的19个观测站进行了缩减。随机森林（RF）用于开发基于非局部回归的MOS模型。结果表明，该模型在所有观测站的降尺度降雨中具有很高的准确性。模型的平均绝对误差（MAE）在0.8-0.39的范围内。标准化均方根误差（NRMSE）在7.4和41之间。²介于0.89和0.99之间。年降雨量的增加范围为7.3–29.5％。与RCP4.5相比，RCP8.5的增加更高。在马来西亚半岛北部观察到最大的增加幅度是20.7–29.5％，而在西南地区的最小增加幅度是7.6–15.2％。