The impact of droughts on vegetation is essentially manifested as the transition of water shortage from the meteorological to ecological stages. Therefore, understanding the mechanism of drought propagation from meteorological to ecological drought is crucial for ecological conservation. This study proposes a method for calculating the probability of meteorological drought to trigger ecological drought at different magnitudes in northwestern China. In this approach, meteorological and ecological drought events during 1982–2020 are identified using the three-dimensional identification method; the propagated drought events are extracted according to a certain spatiotemporal overlap rule, and propagation probability is calculated by coupling the machine learning model and C-vine copula. The results indicate that (1) 46 drought events are successfully paired with 130 meteorological and 184 ecological drought events during 1982–2020, and ecological drought exhibits a longer duration but smaller affected area and severity than meteorological drought; (2) a quadratic discriminant analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which are combined with four-dimensional C-vine copula to construct the drought propagation probability model; and (3) the hybrid method considers more drought characteristics and a more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.

Monitoring and prediction of agricultural drought are paramount to food security at the global and regional scales, particularly under the influence of climate change and anthropogenic activities. Soil moisture is an effective indicator for monitoring and characterizing agricultural drought. Soil moisture (agricultural drought) is mainly affected by precipitation (meteorological drought) and temperature (hot conditions). Owing to the flexibility of vine copulas in handling multidimensional variables by decomposing them into pair copula constructions (PCCs), we propose a novel drought prediction method using three predictors, namely antecedent meteorological drought, previous hot conditions, and persistent agricultural drought, based on the conditional distributions of C‐vine copulas in a four‐dimensional scenario. The proposed model was applied to agricultural drought (characterized by the standardized soil moisture index (SSI)) prediction with 1–2‐months lead time for the summer season (i.e., August at a 6‐months timescale) in China. Taking two severe agricultural drought events that occurred in many regions across China in August of 2006 and 2014 as validation cases, the SSI predictions with 1–2‐months lead time using the conditional C‐vine copulas model were found to be generally consistent with the corresponding historical SSI observations in most parts of China. Performance evaluation using the Nash‐Sutcliffe efficiency (NSE), coefficient of determination (R²), and F₁ score (F₁S) for different climate regions also indicated that this model provided a reliable prediction of agricultural drought for most areas of China. The outcome of this study can serve as a guidance for drought prediction, early warning, and drought mitigation.

Compound climate extremes (here referred to compound dry–hot events and compound pluvial–hot events) result in devastating disasters which threaten water‐food‐energy security. However, in a warming scenario, the risk of occurrence, the quantification of uncertainty, and associated drivers of compound climate extremes—particularly compound pluvial–hot events—have not been fully explored. By leveraging climate model large ensembles, it is revealed that the risk of compound climate extremes is projected to increase 2–3 times over most global land masses in future Representative Concentration Pathway (RCP) 8.5 forcing compared with historical forcing. Increased risks of compound climate extremes are mainly attributed to the changes in temperature and changes in dependence between precipitation and temperature, while the change in precipitation contributing to risk of these two compound climate extremes exhibits approximately spatial complementary. In the warming world, the hot spots of compound dry–hot extremes mainly lie in Europe, South Africa, and the Amazon, while those of compound pluvial–hot extremes mostly lie in the eastern USA, eastern and southern Asia, Australia, and central Africa. These findings help stakeholders and decision makers develop a package of climate change adaptation strategies to manage and mitigate the risk of compound climate extremes.

The amplifying effects of disasters caused by compound dry and hot extremes (dry‐hot events) have attracted widespread attention. This study presents a novel Blended Dry and Hot Events Index (BDHI) considering various dry and hot conditions (i.e., dry/hot, dry/cool, wet/hot, and wet/cool conditions). BDHI was applied to monitor dry‐hot events over global land areas during the 1950–2019 period and its performance was compared against that of the Standardized Compound Event Indicator (SCEI), which includes certain unsatisfactory or unclear features. We found that BDHI comprehensively reflected the characteristics of meteorological drought and temperature anomalies in monitoring dry‐hot events. Many regions around the globe have been experiencing more severe dry‐hot events since the 1990s, especially Russia, the USA, China, India, Australia, southern Africa, Europe, and South America. The proposed index is expected to serve as a potential tool for monitoring dry‐hot events and will be useful for managing and mitigating associated risks.

Compound dry and hot events (CDHEs) have increased significantly and caused agricultural losses and adverse impacts on human health. It is thus critical to investigate changes in CDHEs and population exposure in responding to climate change. Based on the simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), future changes in CDHEs and population exposure are estimated under four Shared Socioeconomic Pathways climate scenarios (SSPs) at first. And then the driving forces behind these changes are analyzed and discussed. The results show that the occurrence of CDHEs is expected to increase by larger magnitudes by the end of the 21st century (the 2080s) than that by the mid‐21st century (2050s). Correspondingly, population exposure to CDHEs is expected to increase significantly responding to higher global warming (SSP3‐7.0 and SSP5‐8.5) but is limited to a relatively low level under the modest emission scenarios (SSP1‐2.6). Globally, compared to 1985–2014, the exposure is expected to increase by 8.5 and 7.7 times under SSP3‐7.0 and SSP5‐8.5 scenarios by the 2080s, respectively. Regionally, Sahara has the largest increase in population exposure to CDHEs, followed by the Mediterranean, Northeast America, Central America, Africa, and Central Asia. The contribution of climate change to the increase of exposure is about 75% by the 2080s under the SSP5‐8.5 scenarios, while that of population change is much lower. The conclusion highlights the importance and urgency of implementing mitigation strategies to alleviate the influence of CDHEs on human society.