In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions.

在这项研究中，贝叶斯分位数回归模型用于研究伊朗大海洋-大气指数与干旱标准降水指数（SPI）之间的遥相关。选择了1952-2014年来自138个天气站点的12个月SPI时间序列作为干旱指数。选择了北大西洋涛动指数（NAO），南方涛动指数（SOI）和厄尔尼诺/南方涛动多元指数（MEI）这三个海洋-大气指数作为协变量。结果表明，NAO对伊朗不同地区和不同地区的干旱影响最弱。拉尼娜条件加剧了西部，里海沿岸地区和南部地区所有SPI分位数的干旱。MEI的正相会显着调节低SPI分位数（即 干旱状况）遍布Zagros地区，里海沿岸地区和南部地区。研究表明，大洋-大气指数的影响对极端干燥和潮湿条件具有不同的影响。