This study assessed the drought susceptibility in Golestan Province, Northeastern Iran, using land use change modeling and climate projections from the CMIP6 framework, under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for 2030–2050. The development of current (2022) and future drought susceptibility maps was based on agrometeorological sample points and 14 environmental factors—such as land use, precipitation, mean temperature, soil moisture, and remote sensing-driven vegetation indices—used as inputs into a machine learning model, maximum entropy. The model showed a very robust predictive capacity, with AUCs for the training and test data of 0.929 and 0.910, thus certifying the model’s reliability. The current analysis identified major hotspots in Gomishan and Aqqala, where 66.12% and 36.12% of their areas, respectively, exhibited “very high” susceptibility. Projections under the SSP scenarios, particularly SSP5-8.5, indicate that the risk of drought will be the most severe in Maraveh Tappeh, where 72.09% of the area exhibits a “very high” risk. The results revealed that Golestan Province is at a crossroads. Rising temperatures, exceeding 35 °C in summer, combined with declining rainfall, intensify agricultural and hydrological droughts. These aggravated risks are compounded with land use transitions from rangelands to bare land, mostly in Aqqala and Gomishan, besides urban expansion in Bandar-e Torkman and Bandar Gaz, all of which face less groundwater recharge and increased surface runoff. Golestan’s drought vulnerability has both local and regional impacts, with its increased susceptibility affecting neighboring communities and ecosystems. Trade, migration, and ecological stresses linked to declining water resources may emerge as critical challenges, requiring regional collaboration for mitigation. Targeted interventions prioritizing sustainable land use practices, regional cooperation, and collaborative strategies are essential to address and mitigate these cascading risks and safeguard vulnerable communities.

Against the backdrop of global warming, heatwaves in China have become more frequent, posing serious risks to public health and socio-economic stability. However, existing identification methods lack precision, and the driving mechanisms of heatwaves remain unclear. This study applies the Excess Heat Factor (EHF) to characterize heatwaves across China from 2013 to 2023, analyzing their spatiotemporal patterns and exploring key drivers such as atmospheric circulation and soil moisture. Key findings reveal significant regional differences: (1) Frequency and Duration—The southeastern coastal regions (e.g., the Yangtze River Delta) experience higher annual heatwave frequencies (1.75–3.5 events) but shorter durations (6.5–8.5 days). In contrast, the arid northwest has both frequent (1.5–3.5 events per year) and prolonged (8.5–14.5 days) heatwaves, while the Tibetan Plateau sees weaker and shorter events. (2) Driving Factors—Heatwaves in the Yangtze River Delta are primarily driven by an intensified subtropical high, leading to subsidence and clear-sky conditions. In Fujian, anomalous low-level winds enhance heat accumulation, while coastal areas show strong soil moisture–temperature coupling, where drier soils intensify warming. Conversely, soil moisture has a weaker influence on the Tibetan Plateau, suggesting a dominant atmospheric control. It is important to note that the EHF index used in this study does not directly account for humidity, which may limit its applicability in humid regions. Additionally, the ERA5 and ERA5-Land reanalysis data were not systematically validated against ground observations, introducing potential uncertainties.