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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.

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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.

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this paper calculated the weights using EFAST method, which considers the coupling relationship between indicators. The connection entropy model that can overcome the uncertainty of evaluation was carried out to evaluate comprehensive water resources carrying capacity. By taking the Yellow River Basin as an example, the weights of the carrying capacity indices were calculated using EFAST method and weight entropy method respectively and made a comparison between the two methods. Finally, the water resources carrying capacity of 61 cities in this basin in 2015 was comprehensively evaluated. The results showed that: EFAST method was more reasonable than entropy weight method in identifying important indicators.Cities whose water resources carrying capacity were at grade I (very high), grade II (higher), grade III (medium), grade IV (lower) and grade V (very low) levels accounted for 0%, 4.9%, 18.1%, 63.9% and 13.1% respectively of the number of cities in the Yellow River Basin. The cities of grades IV and V were distributed in provinces except Qinghai.

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