Drought risk assessment can identify high-risk areas and bridge the gap between impacts and adaptation. However, very few dynamic drought risk assessments and projections have been performed worldwide at high spatial resolution (e.g., 0.5{degree sign} × 0.5{degree sign}) under different greenhouse gas emission scenarios. Here, future global drought risk is projected combing three components (i.e., hazard, exposure, and vulnerability) during 2021-2100 under combined scenarios of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. This study first investigates dynamic drought risks and exposed population and GDP across the six continents (Antarctica is not examined due to data availability). The results show that high-risk regions mainly concentrate in southeastern China, India, Western Europe, eastern United States, and western and eastern Africa. Drought risk will further strengthen in the future under four scenarios, with the highest under SSP5-8.5 and the lowest under SSP3-7.0. Populations exposed to high drought risk for Asia and Africa are much more than other continents. Among four SSP-RCPs, populations exposed to high risk are the largest under SSP3-7.0 for Africa, Asia, and South America, while under SSP5-8.5 for Australia, Europe, and North America. GDP exposed to high drought risk is the largest for Asia among the six continents and the largest under SSP5-8.5 among the SSP-RCPs. The most significant increases in population and GDP under high drought risk both occur in Africa. This study provides a scientific basis for effective adaptation measures to enhance drought resilience in potential high-risk areas.

Compound drought and heatwave (CDHW) events have received considerable attention in recent years due to their devastating effects on human society and ecosystem. In this study, we systematically investigated the spatiotemporal changes of CDHW events for historical period (1951-2014) and four future scenarios (2020-2100) (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) over global land by using Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The sensitivity of the CDHW events to the changes of maximum air temperature and the climatic water balance variables are also examined. The CDHW is defined by integrating monthly standardized precipitation evapotranspiration index (SPEI) and daily maximum temperatures. The results show that the multi-model ensembles project a strong increasing trend in CDHW characteristics over almost all global lands under SSP2-4.5, SSP3-7.0, and SSP5-8.5. A significant increase in CDHW risk will witness across global land areas for the medium to long term future, if there is not aggressive adaptation and mitigation strategies. The results of sensitivity analysis suggest that higher sensitivity of CDHW events to global warming will occur in the future except SSP1-2.6. Particularly, each 1°C global warming increases the duration of the CDHW events by 3 days in the historical period, but by about 10 days in the future period. Overall, this study improves our understanding in the projection of CDHW events and the impacts of climate drivers to the CDHW events under various future scenarios, which could provide support about the risk assessment, adaptation and mitigation strategies under climate change.

Multi-physics ensemble simulations have emerged as a promising approach to ensemble hydrological simulations due to the advantages in process understanding and model development. As a multi-physics ensemble is constructed by perturbing the physics of multi-physics models, the ensemble members share a substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent the independence of the ensemble members affects the ensemble skill gain, especially compared with the multi-model ensemble approach. This study compares a multi-physics ensemble constructed from the Noah land surface model with multi-parameterization options (Noah-MP) with the North American Land Data Assimilation System (NLDAS) multi-model ensemble. The two ensembles are evaluated at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members’ performance, and the inter-member independence is measured by error correlations. The results show that the Noah-MP members outperform, on average, the NLDAS models, especially in the snow-dominated areas. In addition, the best-performing models among the two ensembles are mostly Noah-MP members. However, these two performance superiorities do not lead to the superiority of the ensemble mean. The Noah-MP multi-physics ensemble has a low ensemble skill gain, resulting from a high error correlation among the ensemble members. This study suggests that the methods of ensemble construction and optimization should be improved to also consider inter-member independence, especially for a multi-physics ensemble.