Deciphering Asynchronous Teleconnections: How Dynamic Time Warping Reveals the Hidden Drivers of Iranian Drought

Abstract

Identifying robust precursors for seasonal drought is a central challenge in Earth system science, traditionally approached with linear methods that often fail to capture the complex, asynchronous nature of teleconnections. These methods, by assuming fixed-phase relationships, can overlook or misrepresent crucial climate drivers. This study introduces Dynamic Time Warping (DTW) as a powerful diagnostic framework to overcome this limitation by quantifying similarity between time series irrespective of temporal misalignments. We apply this methodology to investigate the lagged relationships between 19 large-scale climate patterns and seasonal drought variability, derived from the Standardized Precipitation Index, across 13 distinct climatic zones in Iran (1994–2022). Our analysis reveals a significant paradigm shift in understanding Iran's drought drivers. The Western Hemisphere Warm Pool (WHWP), an often-overlooked predictor, emerges as the most dominant and widespread precursory signal, exhibiting statistically significant lead times of up to two seasons (6 months) for over 75% of the country. This contrasts sharply with the conventionally accepted roles of El Niño-Southern Oscillation and North Atlantic Oscillation. The DTW framework also effectively identifies regions of multiple teleconnection influences (“climatic crossroads”) and areas where local dynamics prevail (“silent zones”). Our findings demonstrate that time-adaptive modeling is essential for uncovering hidden drivers in climate systems, offering a new pathway to enhance the physical basis and predictive skill of seasonal forecasting models. This approach provides a transferable methodology for reassessing climate teleconnections globally.