Due to the drought negative impacts, accurate forecasting of drought indices is important. This study focused on the short- to long-term Standardized Precipitation Index (SPI) forecasting in sites with different climates using newly integrated hybrid pre-post-processing techniques. Four sites in Iran's northwest were selected and the SPIs series with time scales of 3, 9, and 24 months were forecasted during the period of 1978–2017. For improving the modeling efficiency, wavelet transform and ensemble empirical mode decomposition (EEMD) pre-processing methods were used. In this regard, temporal features of the SPIs series were decomposed via wavelet transform (WT), then, the obtained sub-series were further broken down into intrinsic mode functions using EEMD. Also, simple linear averaging and nonlinear neural ensemble post-processing methods were applied to ensemble the outputs of hybrid models. The results showed that data pre-processing enhanced the models' capability up to 40%. Also, integrated pre-post-processing models improved the models' efficiency by approximately 50%. The root mean square errors' criteria distribution range decreased from 0.337–1.03 (in raw data) to 0.195–0.714 (in decomposed data). The results proved the capability of applied methods in modeling the SPIs series. In increasing the models' accuracy, data pre-processing was more effective than data post-processing.

由于干旱的不利影响，准确预测干旱指数很重要。这项研究的重点是使用新集成的混合后处理技术在不同气候的地点进行短期至长期的标准化降水指数（SPI）预报。选择了伊朗西北部的四个地点，并在1978-2017年期间预测了时间分别为3、9和24个月的SPI系列。为了提高建模效率，使用了小波变换和集成经验模式分解（EEMD）预处理方法。在这方面，通过小波变换（WT）分解SPI系列的时间特征，然后使用EEMD将获得的子系列进一步分解为固有模式函数。也，简单的线性平均和非线性神经集合后处理方法用于集合混合模型的输出。结果表明，数据预处理将模型的功能提高了40％。此外，集成的预后处理模型将模型的效率提高了约50％。均方根误差的标准分布范围从0.337–1.03（在原始数据中）降低到0.195–0.714（在分解数据中）。结果证明了所应用方法在SPI系列建模中的能力。在提高模型的准确性方面，数据预处理比数据后处理更有效。集成的预后处理模型将模型的效率提高了约50％。均方根误差的标准分布范围从0.337–1.03（在原始数据中）降低到0.195–0.714（在分解数据中）。结果证明了所应用方法在SPI系列建模中的能力。在提高模型的准确性方面，数据预处理比数据后处理更有效。集成的预后处理模型将模型的效率提高了约50％。均方根误差的标准分布范围从0.337–1.03（在原始数据中）降低到0.195–0.714（在分解数据中）。结果证明了所应用方法在SPI系列建模中的能力。在提高模型的准确性方面，数据预处理比数据后处理更有效。