Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation ( R 2 >0.63; R 2 , determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature ( R 2 >0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.

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使用PDSI和SDSM预测干旱和半干旱地区的气象干旱：以伊朗法尔斯省为例

干旱是最严重的环境灾害之一，特别是在干旱和半干旱地区。干旱指数作为旨在应对干旱现象的管理实践工具，在世界范围内得到广泛应用。这些指标之一是帕尔默干旱严重程度指数 (PDSI)，该指数在世界许多地方用于评估干旱情况和持续性。本研究根据伊朗法尔斯省 6 个气象站的气象数据，利用 PDSI 对 1995-2014 年伊朗法尔斯省的干旱状况进行了评估。统计降尺度模型（SDSM）被用于应用法尔斯省大气环流模型的输出结果。为实施气候资料的资料处理和预测，以1995-2014年的统计期为监测期，预测期为 2019-2048 年的统计期。结果表明，模拟降水（R 2 >0.63；R 2 决定系数；MAE<0.52；MAE，平均绝对误差；RMSE<0.56；RMSE，均方根误差）与温度（R 2 >0.95, MAE<1.74, and RMSE<1.78) 与台站的观测数据。干旱监测模型的结果表明，与历史数据相比，未来 30 年干旱期将增加。研究表明，在两种未来情景代表性浓度路径（RCP4.5和RCP8.5）下，气象站Abadeh和Lar在预测期内干旱最严重。根据验证期和效率标准的结果，