In recent years, stable isotopes of the water molecule (oxygen-18 and deuterium) have become a useful tool for tracking the water cycle. The concentration of these tracers changes with variations of water molecules within the water cycle. Due to this feature of isotopes, global large-scale isotope models have been developed. On the other hand, numerous local and global networks have been created in order to monitor the concentration of precipitation isotopes. The main problem with the simultaneous use of these local stations and the large-scale isotope datasets is their temporal and spatial mismatch. To use both isotope databases for monitoring the hydrological cycle in local scale, it is necessary to downscale the large-scale models' outputs. In this research, a downscaling approach is proposed for isotopes' concentrations using three statistical models, including multiple linear regression, generalized linear and weighting least square regression models. The results indicate that the implementation of the statistical downscaling method in the case of information preprocessing based on the seasonal changes, their spatial variations and a suitable method selection is a useful tool for monitoring the climate changes of a region according to the information on the stable oxygen-18 isotope.

近年来，水分子（18号氧和氘）的稳定同位素已成为跟踪水循环的有用工具。这些示踪剂的浓度随水循环中水分子的变化而变化。由于同位素的这一特征，已经开发了全球大规模同位素模型。另一方面，为了监测降水同位素的浓度，已经建立了许多本地和全球网络。同时使用这些本地站和大规模同位素数据集的主要问题是它们的时间和空间失配。为了使用两个同位素数据库来监测局部规模的水文循环，有必要缩减大规模模型的输出。在这项研究中，提出了一种同位素缩小的方法 浓度使用三种统计模型，包括多元线性回归，广义线性和加权最小二乘回归模型。结果表明，在基于季节变化，其空间变化和适当方法选择的信息预处理中，采用统计缩减方法是根据区域稳定信息监测区域气候变化的有用工具。氧18同位素。