Climate changes and its undesirable impacts (such as melting glaciers, the occurrence of floods, hurricanes and droughts, etc.) are among the most important disasters that human beings have faced in recent decades. Considering the important role of the meteorological parameters on climate change, in this research the effectiveness rate of various meteorological parameters including the mean minimum and maximum annual temperature (Max-Temp and Min-Temp), the mean annual temperature (M-Temp), the mean annual sunshine (Sunshine), the mean annual relative humidity (Humidity), the mean annual wind speed (Wind) and the mean of annual precipitation (Rainfall) on United Nations Environmental Programme (UNEP) aridity index was assessed and prioritized using the Backward Multiple Ridge Regression (BMRR). In this study, the meteorological data series of 25 synoptic stations in Iran with different climate conditions during 1967–2017 was used. The results indicated that the BMRR method had a nice capability to predict the UNEP index using the above-mentioned meteorological parameters (the linear regression between observed and predicted the UNEP index had no difference with perfect reliable line (Y = X) at 0.05 significant levels and the R² between two mentioned data series were significant at 0.01 levels at all stations). According to the results, Rainfall, Wind and Max-Temp parameters were the most effective parameters on the UNEP aridity index, respectively and the Min-Temp, M-Temp and Sunshine parameters were the least effective parameters on the UNEP index, respectively. Therefore, it is suggested that all human activities that have direct or indirect effects in the increasing temperature and reducing the rainfall must be revised and optimized based on the principles of sustainable development.

气候变化及其不良影响（例如冰川融化，洪水，飓风和干旱等）是人类近几十年来面临的最重要的灾难。考虑到气象参数在气候变化中的重要作用，在这项研究中，各种气象参数的有效率包括最低和最高年平均气温（Max-Temp和Min-Temp），年平均气温（M-Temp），联合国环境规划署（UNEP）干旱指数评估了年平均日照（Sunshine），年平均相对湿度（Humidity），年平均风速（Wind）和年平均降水量（Rainfall），并使用向后多元岭回归（BMRR）。在这个研究中，使用了1967-2017年伊朗25个天气不同的天气观测站的气象数据序列。结果表明，BMRR方法具有使用上述气象参数预测环境署指数的良好能力（观察到的和预测的环境署指数之间的线性回归与0.05显着水平下的完美可靠线（Y = X）没有差异）和R²两者之间提到的数据系列分别在在所有的站0.01水平）显著。根据结果​​，降雨，风和最大温度参数分别是对环境署干旱指数最有效的参数，最小温度，中间温度和阳光参数分别是对环境署指数最不有效的参数。因此，建议必须根据可持续发展的原则修改和优化对温度升高和降雨减少具有直接或间接影响的所有人类活动。