Compound dry and hot conditions frequently cause large impacts on ecosystems and societies worldwide. A suite of indices is available for the assessment of droughts and heatwaves, yet there is no index available for incorporating the joint variability of dry and hot conditions at the sub-monthly scale. Here we introduce a daily-scale index, called the standardized compound drought and heat index (SCDHI), to assess compound dry-hot conditions. The SCDHI is based on a daily drought index (the standardized antecedent precipitation evapotranspiration index – SAPEI), the daily-scale standardized temperature index (STI), and a joint probability distribution method. The new index is verified against real-world compound dry and hot events and associated observed vegetation impacts in China. The SCDHI can not only capture compound dry and hot events at both monthly and sub-monthly scales, but is also a good indicator for associated vegetation impacts. Using the SCDHI, we quantify the frequency, severity, duration, and intensity of compound dry-hot events during the historical period and assess the ability of climate models to reproduce these characteristics in China. We find that compound events whose severity is at least light and which last longer than 2 weeks generally persisted for 20–35 d in China. Southern China suffered from compound events most frequently, and the most severe compound events were mainly detected in this region. Climate models generally overestimate the frequency, duration, severity, and intensity of compound events in China, especially for western regions, which can be attributed to a too strong dependence between the SAPEI and STI in those models. The SCDHI provides a new tool to quantify sub-monthly characteristics of compound dry and hot events and to monitor their initiation, development, and decay. This is important information for decision-makers and stakeholders to release early and timely warnings.

Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings.

While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local hydro‐climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP‐SVR, ASFP‐ELM, and ASFP‐RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1‐ and 3‐month lead times. Results show that the ASFP‐ELM model can effectively predict space‐time evolutions of drought events with satisfactory skills, outperforming the ASFP‐SVR and ASFP‐RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems.

Understanding the spatial and temporal patterns of drought is essential for mitigating drought‐induced impacts. To date, less attention is paid to drought characterization and changes across global river basins within a 3D clustering drought identification framework. Here, we characterized drought events across 59 global river basins during 1979–2020 based on standardized precipitation evapotranspiration index and a three‐dimensional clustering method, together with exploration of relationships between drought indicators. The results show that drought characteristics did not change significantly over time in most basins, but the frequency tended to decrease in the Middle East and North Africa and showed increase at high latitudes. Droughts in Amazon, Nile and La Plata basins are severer than other basins with higher severities on the whole. Moreover, for most all basins, drought affected area and severity both increased with duration.