Selection of potential causal variables (PCVs) from a pool of many possibly associated variables is a critical issue since it can significantly affect the performance of any statistical downscaling model. Generally, the variable to be downscaled is associated with many other hydrologic and climatic (aka hydroclimatic) variables. Most of the existing approaches, such as correlation analysis (CA), partial correlation analysis (PaCA), and stepwise regression analysis (SRA), rely mostly on the mutual association for the selection of PCVs. However, none of these approaches investigate the detailed dependence structure that may be helpful in eliminating the unwanted information and efficiently selecting the PCVs for downscaling the target variable. In this study, the effectiveness of graphical modeling (GM) approach is explored for the selection of the PCVs as GM can effectively identify the detailed conditional independence structure among all the associated variables. For demonstration, downscaling of monthly precipitation is undertaken using the PCVs, identified by CA, PaCA, SRA, and the proposed GM approach. Two different downscaling models, namely statistical downscaling model (SDSM) and support vector regression (SVR)–based downscaling model, are utilized. The results show that the PCVs identified through the proposed GM approach provides consistent as well as robust performance, across different regions and seasons, due to its ability to capture the complete conditional indepedence structure among the variables. The downscaled monthly precipitation obtained using the proposed approach is better matching with the observed data in terms of the mean, variance as well as the probability distribution. Overall, this study recommends the GM approach for the identification of the PCVs for the downscaling models.

从许多可能相关联的变量中选择潜在因果变量（PCV）是一个关键问题，因为它会显着影响任何统计缩减模型的性能。通常，要缩小比例的变量与许多其他水文和气候（又称水气候）变量相关。大多数现有方法，例如相关分析（CA），偏相关分析（PaCA）和逐步回归分析（SRA），都主要依赖于相互关联来选择PCV。但是，这些方法都没有研究详细的依存关系结构，这可能有助于消除不需要的信息并有效地选择PCV以缩小目标变量。在这个研究中，探讨了图形建模（GM）方法在选择PCV方面的有效性，因为GM可以有效地识别所有相关变量之间的详细条件独立性结构。为了证明这一点，使用了CA，PaCA，SRA和拟议的GM方法确定的PCV对月降水量进行了缩减。利用了两种不同的缩减模型，即基于统计的缩减模型（SDSM）和基于支持向量回归（SVR）的缩减模型。结果表明，通过拟议的GM方法确定的PCV在不同区域和季节之间均具有一致且稳健的性能，这是由于其能够捕获变量中完整的条件独立结构。使用所提出的方法获得的降尺度的月降水量在均值，方差以及概率分布方面与观测数据更好地匹配。总体而言，这项研究建议采用通用方法来确定降尺度模型的PCV。