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 changes of CDHW events in multi‐spatiotemporal scales 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 responses of the CDHW events to the changes of maximum air temperature and the climatic water balance variable are also examined. The results show that the multi‐model ensembles project a significant increasing trend in CDHW characteristics over almost all global lands under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, especially across northern North‐America, Caribbean, Mediterranean and Russian‐Arctic, there is a stronger increasing trend. A significantly increasing CDHW risk will occur across most global land for the medium to long term future without aggressive adaptation and mitigation strategies. The results of path analysis suggest that temperature is the dominant factor influencing CDHW events. Additionally, higher sensitivity of CDHW events to global warming will occur in the future. 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 supports about the risk assessment, adaptation and mitigation strategies under climate change.

Meteorological to hydrological drought propagation has been widely studied to reflect the relationship between these drought categories and better understand drought mechanisms. However, global warming may alter the drought propagation features, which are not fully understood. This study aims to investigate changes in meteorological and hydrological drought conditions, especially their propagation features in 1.5–3.0°C warmer climates for 8,655 watersheds globally. First, the three‐month scale standardized precipitation index and the standardized runoff index are calculated based on the precipitation simulated by the 15 global climate models and the runoff simulated by the four hydrological models, respectively. Drought events are then identified using the run theory, followed by the calculation of drought propagation features (i.e., pooling, lag, and lengthening) for matched meteorological and hydrological droughts. As a result, both meteorological and hydrological drought conditions (i.e., duration and severity) would relieve in warmer climates due to increased precipitation for regions excluding Western North America, South America, the Mediterranean, Southern Africa, East Asia, and Australia. However, the drought conditions would be more severe during drought propagation from meteorological to hydrological droughts over most regions. During drought propagation, the worsening drought conditions over half of the regions would be more serious first and then relieved with the rising temperature. These results indicate that efforts to slow down global warming can suppress the deterioration of drought conditions in the propagation.

Drought risk assessment can identify high‐risk areas and bridge the gap between impacts and adaptation. It is thus vital to investigate changes in drought risk and exposed social economy under climate change. Here, the future global meteorological drought risk is projected for the 2021–2100 period under four combined scenarios of Representative Concentration Pathways and Shared Socioeconomic Pathways (SSPs), i.e., SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. And then the exposed population and Gross Domestic Product (GDP) under high risk are analyzed. Drought risk will further strengthen in the future under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, with large increases distributed in South Asia, the Mediterranean, East Asia, Southeast Asia, and Central America. The strongest increasing trends in drought risk are concentrated in equatorial regions including South Asia, West Africa, and Central America. A large percentage of the population and economy are exposed to high drought risk. The exposed population under the highest risk level is the largest under SSP5‐8.5 and the smallest under SSP3‐7.0 for regions in North America, while being the largest under SSP3‐7.0 for other regions. The exposed GDP under the highest risk level is the highest under SSP5‐8.5 and the lowest under SSP3‐7.0, while the disparity among diverse scenarios is larger for the rich regions than the developing ones.

Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection of the downscaling method and the determination of the environmental variables, namely, the influencing factors of soil moisture. This study attempted to utilize machine learning and data mining algorithms to downscale the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data from 25 km to 1 km and compared the advantages and disadvantages of the random forest model and the Cubist algorithm to determine the more suitable soil moisture downscaling method for the middle and lower reaches of the Yangtze River Basin (MLRYRB). At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable for the downscaling models. In contrast, variables, such as albedo and evapotranspiration, are infrequently applied; nevertheless, this study selected these two environmental variables, which have a considerable impact on soil moisture. Thus, the selected environmental variables in the downscaling process included the longitude, latitude, elevation, slope, NDVI, daytime and nighttime land surface temperature (LST_D and LST_N, respectively), albedo, evapotranspiration (ET), land cover (LC) type, and aspect. This study achieved downscaling on a 16-day timescale based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. A comparison of the random forest model with the Cubist algorithm revealed that the R² of the random forest-based downscaling method is higher than that of the Cubist algorithm-based method by 0.0161; moreover, the root-mean-square error (RMSE) is reduced by 0.0006 and the mean absolute error (MAE) is reduced by 0.0014. Testing the accuracies of these two downscaling methods showed that the random forest model is more suitable than the Cubist algorithm for downscaling AMSR-E soil moisture data from 25 km to 1 km in the MLRYRB, which provides a theoretical basis for obtaining high spatial resolution soil moisture data.