<p>Drought is a recurrent climatic phenomenon that has significant ecological, agricultural and economic effects all over the world, especially in dry and low rainfall regions like Iran. The article aims to explore the differences between parametric and non-parametric methods in calculating the Standardized Precipitation Index (SPI). Additionally, it introduces an innovative application of image processing techniques for result evaluation and proposes a novel method based on Rasterized Inverse Distance Weighting to analyze variations in drought characteristics—such as duration, severity, and intensity—across Iran. Three drought characteristics were calculated by parametric and non-parametric methods using observed stations data and CanESM5 model, Scenario 8.5 and Scenario 2.6. In addition, different time scales including short-term (SPI-3), medium-term (SPI-6) and long-term (SPI-12) have been used to investigate the difference in the trend of drought characteristics. These indices were interpolated and evaluated in the entire country using the Inverse Distance Weighting (IDW) method. The study shows that time series from both the parametric Gamma distribution and nonparametric approaches are well correlated but have different drought indicator trends. The results highlight the differences in performance between parametric and non-parametric methods in estimating drought characteristics including duration, intensity, and severity. Furthermore, due to the varying prevailing weather currents in the north and south, as well as the different climate types in the west and east of Iran, statistical indices show different results when using parametric and non-parametric methods. The findings highlight the need for precise drought monitoring to improve early warnings, manage water efficiently, and support resilient agriculture.</p>