Drought is one of the greatest environmental and social challenges globally, with serious implications for food security and water resource management. This study focuses on short-term drought Projection in Iran using Shared Socioeconomic Pathways (SSP) scenarios. Data from 95 synoptic stations for 1985–2014 served as the baseline. Five General Circulation Models (GCMs) GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, and UKESM1-0-LL were used to project precipitation. The data underwent Kriging interpolation and bias correction. To reduce uncertainty, a multi-model ensemble approach was applied. Results show that ensemble-processed data align closely with observations, exhibiting reduced errors and higher correlation coefficients. The ensemble model outperformed individual models. Analysis of baseline precipitation patterns revealed strong influences from geographic and climatic factors, with high precipitation in the Zagros Mountains and southwestern Caspian Sea coasts during cold months. Projections for 2021–2040 under the SSP5-8.5 scenario indicate significant shifts: increased precipitation is expected in high-altitude regions and along the Caspian coasts, though rising temperatures may offset these benefits. Conversely, precipitation is projected to decline in northwestern and central Iran, especially in spring and summer. These changes pose significant challenges for water and agricultural management, underscoring the necessity for proactive planning. By providing more precise projections, this study supports policymakers in developing effective strategies to cope with drought.
Introduction
The escalating frequency of extreme climatic events represents one of the most palpable consequences of anthropogenic climate change, posing severe threats to ecosystem integrity, public health, and economic stability. Among these hazards, drought is globally recognized as the most detrimental due to its creeping onset, extensive spatial footprint, and prolonged duration. Iran, situated within the global arid and semi-arid belt, has historically contended with water scarcity. However, recent decades have witnessed a convergence of climatic shifts and managerial challenges, propelling the nation toward a pervasive water crisis. Predictive models identify Iran as a highly vulnerable region to future hydrological anomalies. While previous investigations predominantly relied on the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the advent of CMIP6 offers enhanced physical parameterization and higher spatial resolution. This study employs a strategic approach by utilizing CMIP6 models under the high-emission SSP5-8.5 scenario. This scenario serves as a systemic "stress test" for Iran’s water resource management infrastructure, evaluating resilience against the most severe plausible climatic trajectory. The primary objective is to analyze the spatiotemporal patterns of short-term droughts for the near-future horizon (2021–2040) and to delineate critical hotspots, thereby facilitating a paradigm shift from reactive crisis management to proactive risk mitigation.
Material and Methods
The study area encompasses the entire territory of Iran. Observational data for precipitation and temperature were obtained from 95 synoptic stations for the baseline period (1985–2014) to validate model performance. For future projections (2021–2040), outputs from five CMIP6 General Circulation Models (GCMs) specifically GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2 and UKESM1-0-LL—were utilized under the SSP5-8.5 scenario. To address the scale mismatch between GCM outputs and local observations, a robust downscaling procedure was implemented using Kriging interpolation to generate a 19×19 km grid, followed by bias correction via the Delta Correction Factor (DCF) method. To mitigate structural uncertainties inherent in individual models, a weighted Multi-Model Ensemble (MME) approach was adopted, with weights assigned based on the Pearson correlation coefficients between model outputs and historical observations. The Standardized Precipitation-Evapotranspiration Index (SPEI) was selected to characterize drought conditions, as its incorporation of potential evapotranspiration (calculated via the Thornthwaite method) makes it particularly sensitive to the thermal amplification of drought under global warming. Model accuracy and the ensemble performance were rigorously evaluated using Taylor diagrams and statistical metrics, including RMSE, MAE, MBE and R².
Results and Discussion
The validation analysis, utilizing Taylor diagrams and statistical metrics, demonstrated that the Multi-Model Ensemble (MME) approach significantly mitigated the structural uncertainties inherent in individual CMIP6 models. While individual models exhibited biases ranging from 28% to 78% in parameter estimation, the ensemble-adjusted data showed negligible deviations (less than 2%) and high correlation coefficients, indicating a robust alignment with baseline observational records (1985–2014).
Baseline analysis confirmed the topographic control over moisture distribution, identifying the Zagros Mountains and the Caspian coast as primary precipitation zones, in contrast to the arid central and eastern basins. However, projections for the near-future horizon (2021–2040) under the SSP5-8.5 scenario reveal critical shifts in spatiotemporal precipitation patterns. Although a potential increase in precipitation is projected for high-altitude regions and the southern Caspian coast during specific cold months (e.g., January), this trend is reversed during critical growing seasons. Notably, northwestern, central and southeastern Iran are projected to experience precipitation deficits and negative anomalies during spring and summer months.
The most pivotal finding emerges from the SPEI analysis, which underscores the thermodynamic dominance of climate change in the region. The results indicate that the substantial rise in surface temperatures and the consequent surge in atmospheric evaporative demand will likely offset any localized precipitation gains, driving a negative water balance. This phenomenon signifies a transition toward a "Hot Drought" regime, characterized by increased frequency and severity of drought events across most of the country. Spatial risk mapping identifies southern regions (such as the Persian Gulf coast), eastern, and parts of central Iran as vulnerability "hotspots." In these areas, the recurrence interval of severe droughts is projected to shorten, while wet periods diminish. These spatial patterns display strong concordance with previous literature, confirming that the primary driver of future droughts in Iran is shifting from precipitation deficits to thermal stress and intensified evapotranspiration. This trajectory poses profound challenges for rainfed agriculture and reservoir management, particularly during the warm season when water demand peaks.
Conclusion
This study demonstrates the efficacy of the Multi-Model Ensemble (MME) approach utilizing CMIP6 datasets under the SSP5-8.5 scenario in minimizing simulation uncertainties and enhancing the fidelity of short-term drought projections for Iran. The results indicate that for the 2021–2040 horizon, although precipitation patterns exhibit regional variability, the dominant climatic driver will be the substantial rise in surface temperatures; this warming trend is projected to amplify potential evapotranspiration rates, effectively negating localized precipitation gains and driving a transition toward more frequent and widespread "hot droughts." Spatiotemporal analysis identifies the southern, eastern, and central regions of the country as critical hotspots of vulnerability, where the intensification of drought severity is expected to be most pronounced. These findings underscore the imperative for a strategic paradigm shift from reactive crisis management to proactive risk management, necessitating robust policy interventions that prioritize demand-side water management, climate-adaptive agricultural practices and the implementation of advanced early warning systems to safeguard national water and food security against escalating climatic stresses.