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Contemporary understanding of the impacts of climate change on global drought characteristics (e.g., intensities, extents) is still limited and not well understood. This knowledge is critical because projected changes in climate are expected to impact on future water availability as well as influence decisions on how water resources are allocated. The main aim of this study is to improve understanding of drought characteristics (extents and duration) in the Anthropocene where rapid changes in the environment are caused by the composite influence of human activities and climate change. Multi-scale earth observation data (1980−2020) and the Coupled Model Intercomparison Project Phase 6 climate models, which incorporate the Shared Socioeconomic Pathways (2040−2070and 2070−2100) are used to assess these characteristics as well as identify climatic hotspots where changes in drought characteristics could drive groundwater hydrology. Results show that towards the end of the 21st century, global land areas under drought will significantly decrease but their durations will not. Generally, there is evidence of significant decline in the proportion of areas that will experience various drought intensities (moderate, severe and extreme drought) in the future and for each category, drought affected areas will not reach 30% on average. Moreover, some regions are potential hotspots of climate–groundwater interactions where drought events could directly impact on groundwater. This is because of the varying degree of strong correlations (positive and negative) between climate and groundwater data in some areas (e.g., Australia, Europe, Southern Africa, Asia). The relatively strong negative correlations in some of these hotspots are indicative of the presence of considerable lags, that could be caused by aridity as well as human groundwater footprints.

The terrestrial water storage anomaly (TWSA) from the previous Gravity Recovery and Climate Experiment (GRACE) covers a relatively short period (15 years) with several missing periods. This study explores the boosted regression trees (BRT) and the artificial neural network (ANN) to reconstruct the TWSA series between 1982 and 2014 over the Yangtze River basin (YRB). Both algorithms are trained with several hydro-climatic variables (e.g., precipitation, soil moisture, and temperature) and climate indices for the YRB. The results from this study show that the BRT is capable of reconstructing TWSA and shows Nash–Sutcliffe efficiency (NSE) of 0.89 and a root-mean-square error (RMSE) of 18.94 mm during the test stage, outperforming ANN in about 2.3% and 7.4%, respectively. As a step further, the reliability of this technique in reconstructing TWSA beyond the GRACE era was also evaluated. Hence, a closed-loop simulation using the artificial TWSA series over 1982–2014 under the same scenarios for the actual GRACE data shows that BRT can predict TWSA (NSE of 0.92 and RMSE of 6.93 mm). Again, the BRT outperformed the ANN by approximately 1.1% and 5.3%, respectively. This study provides a new perspective for reconstructing and filling the gaps in the GRACE–TWSA series over data-scarce regions, which is desired for hydrological drought characterization and environmental studies. BRT offers such an opportunity for the GRACE Follow-On mission to predict 11 months of missing TWSA data by relying on a limited number of predictive variables, hence being adjudged to be more economical than the ANN.