The changes and fluctuations in climate variables, especially temperature, that subsequently affect human activities and natural environments are one of the critical topics in scientific societies. Therefore, time variability analysis of average temperature is an important concept in climate studies, particularly in environmental planning and management at various levels. The main purpose of the present study is to detect the temporal and spatial variability of average monthly temperature in Iran during the period 2015–2060 based on the RCP4.5 and RCP 8.5 scenarios simulated by the CMIP5 atmospheric general circulation models. For this purpose, the monthly average temperature data relative to four CMIP5 models, including CMCC-CM, CESMI-BGC, CCSM4, and MRI-CGCM3 models, for the period 1987–2060, and the observed monthly average temperature for the period 1987–2014 measured at 88 synoptic stations distributed all over Iran were used. The accuracy of the CMIP5 models in simulating the historical data for the period 1987–2005 was evaluated against the observed data at the synoptic stations using R, R2, RMSE, bias, EF, NARMSE, slope, and IA statistics. To study the temporal and spatial variation of temperature relative to the historical and future periods across Iran, the statistical spatial variance model was applied. The result showed that the historical temperatures estimated by all CMIP5 models are highly correlated with the observed temperatures all over Iran, but the accuracy of the MRI-CGCM3 model was found slightly higher than the other three models in most areas of Iran. In general, the results showed that the temperature estimated by the selected models and scenarios have a very high correlation with the observed data in most parts of Iran, especially in the mountainous areas of the western country. In the coastal areas of southern and northern Iran, the accuracies of the models somehow decreased which can be attributed to the complex topographical structure of these areas and/or the other effective local features that have not been incorporated in the models. The results showed that the highest temporal variability of temperature has occurred in winter months and partly in the autumn, and the spatial variability of temperature has been observed primarily in the mountainous areas of Iran. The investigation of the temperature variability in the future decades (2015–2059) was found in parallel with the temporal variations of temperature in the present period, considering that the highest temperature variability will occur in winter and somehow in the autumn, mostly in the mountainous areas of Iran. Generally, in most parts of the country, the air temperature in future decades will have an increasing tendency in all four seasons of the year.

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CMIP5 GCM 模型预测的 21 世纪伊朗温度的时空变化

气候变量，特别是温度的变化和波动，随后影响人类活动和自然环境，是科学社会的关键课题之一。因此，平均温度的时变分析是气候研究中的一个重要概念，特别是在各级环境规划和管理中。本研究的主要目的是基于 CMIP5 大气环流模型模拟的 RCP4.5 和 RCP 8.5 情景，检测 2015-2060 年期间伊朗平均月气温的时空变化。为此，1987-2060 年期间与四个 CMIP5 模型（包括 CMCC-CM、CESMI-BGC、CCSM4 和 MRI-CGCM3 模型）相关的月平均温度数据，并使用了 1987-2014 年期间在伊朗各地分布的 88 个天气站测量的月平均温度。使用 R、R2、RMSE、偏差、EF、NARMSE、斜率和 IA 统计数据，根据天气站的观测数据评估了 CMIP5 模型在模拟 1987-2005 年期间历史数据的准确性。为了研究伊朗相对于历史和未来时期的温度时空变化，应用了统计空间方差模型。结果表明，所有CMIP5模型估计的历史温度与伊朗全境观测温度高度相关，但在伊朗大部分地区，MRI-CGCM3模型的精度略高于其他三个模型。一般来说，结果表明，所选模型和情景估计的温度与伊朗大部分地区的观测数据具有非常高的相关性，尤其是在西部国家的山区。在伊朗南部和北部的沿海地区，模型的精度有所下降，这可能是由于这些地区的复杂地形结构和/或模型中未包含的其他有效局部特征。结果表明，温度的最高时间变异发生在冬季，部分发生在秋季，温度的空间变异主要发生在伊朗的山区。未来几十年（2015-2059 年）温度变化的调查与当前时期温度的时间变化同时进行，考虑到最高的温度变化将发生在冬季和秋季，主要发生在山区。伊朗地区。总体来看，在全国大部分地区，未来几十年全年四个季节的气温均呈上升趋势。