Short-term drought forecasting in Iran using multi-source machine learning: an assessment of autoregressive, teleconnection-driven, and hybrid paradigms

Accurate meteorological drought forecasting is a major scientific challenge due to its complexities and the competition among modeling paradigms. For the first time, this study provides a comprehensive and comparative assessment at the national scale of Iran to determine the relative superiority or synergy of three competing paradigms: (1) autoregressive (based on temporal memory), (2) teleconnection-driven (based on large-scale climate drivers), and (3) hybrid. Using 30-year precipitation data from 96 synoptic stations and 19 global climate indices, the performance of nine machine and deep learning models was tested for forecasting the Standardized Precipitation Index (SPI) at 1-, 2-, and 3-month lead times. The results conclusively reject the idea of a single paradigm's universal superiority, demonstrating that the optimal model structure is highly location-dependent. The hybrid approach, integrating temporal memory with large-scale climate drivers, prevailed in the vast arid and semi-arid regions of Iran, while the standalone paradigms performed best in specific “climate niches” (such as the northern and southern coasts). Among the models, Random Forest (RF) was the most robust and stable algorithm. These findings underscore the necessity of transitioning from “one-model-fits-all” approaches towards developing adaptive, region-centric modeling frameworks to enhance drought early warning systems.