The present study proposes a climate change assessment tool based on a statistical downscaling (SD) approach for describing the linkage between large-scale climate predictors and observed daily rainfall characteristics at a local site. The proposed SD of the daily rainfall process (SDRain) model is based on a combination of a logistic regression model for representing the daily rainfall occurrences and a nonlinear regression model for describing the daily precipitation amounts. A scaling factor (S_R) and correction coefficient (C_R) are suggested to improve the accuracy of the SDRain model in representing the variance of the observed daily precipitation amounts in each month without affecting the monthly mean precipitation. SDRain facilitates the construction of daily precipitation models for the current and future climate conditions. The tool is tested using the National Center for Environmental Prediction re-analysis data and the observed daily precipitation data available for the 1961–2001 period at two study sites located in two completely different climatic regions: the Seoul station in subtropical-climate Korea and the Dorval Airport station in cold-climate Canada. Results of this illustrative application have indicated that the proposed functions (e.g. logistic regression, S_R, and C_R) contribute marked improvement in describing daily precipitation amounts and occurrences. Furthermore, the comparison analyses show that the proposed SD method could provide more accurate results than those given by the currently popular SDSM method.

本研究提出了一种基于统计缩减（SD）方法的气候变化评估工具，用于描述大规模气候预测因子与当地观测到的每日降雨特征之间的联系。拟议的日降水过程的SD（SDRain）模型是基于用于表示日降水量的逻辑回归模型和用于描述日降水量的非线性回归模型的组合。比例因子（S _R）和校正系数（C _R建议使用）来提高SDRain模型在表示每月观测到的每日降水量方差时的准确性，而不会影响每月平均降水量。SDRain有助于构建当前和未来气候条件的每日降水模型。使用国家环境预测中心的再分析数据和1961-2001年期间观测到的每日降水量数据对这套工具进行了测试，该数据位于两个完全不同的气候区域：韩国亚热带气候的汉城站和韩国在寒冷气候加拿大的多瓦尔机场驻地。此说明性应用程序的结果表明，建议的功能（例如，逻辑回归，S _R和C _R）在描述每日降水量和发生方面做出了显着改善。此外，比较分析表明，所提出的SD方​​法比当前流行的SDSM方法所提供的结果更准确。