This paper introduces Prec-DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine-learning based method for statistical downscaling of precipitation. Prec-DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge-radar precipitation data at 0.125° from NLDAS-2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5°, and 1°). Quantitative evaluation of these experiments demonstrates that Prec-DWARF consistently outperforms the baseline (i.e., bilinear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec-DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine-scale spatial structure, especially for the 1° experiments. Prec-DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate importance analysis shows that the most important predictors for the downscaling are the coarse-scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec-DWARF and machine-learning based techniques in general for the statistical downscaling of precipitation.

The drought‐pluvial seesaw—defined as the phenomenon of pluvials (wet spells) following droughts (dry spells)—magnifies the impact of individual pluvial and drought events yet has not been systematically evaluated, especially at the global scale. We apply an event coincidence analysis to explore the aggregated seesaw behavior based on land surface model simulations for the past seven decades (1950–2016). We find that globally, about 5.9% and 7.6% of the land surface have experienced statistically significant (p<0.10) drought‐pluvial seesaw behavior during the boreal spring‐summer and fall‐winter, with an average 11.1% and 11.4% of all droughts being followed by pluvials in the following season, respectively. Although this global frequency pattern is modest and coherent changes cannot be detected at the subcontinental scale, local hot spots of drought‐pluvial seesaw have become more frequent than either droughts or pluvials alone in the last three decades, albeit with a small percentage of area coverage.

Vegetation restoration over degraded drylands has considerable climate, carbon and ecosystem benefits, yet its water impacts remain contentious. Previous studies suggest that extra vegetation in drylands could lead to decreased soil moisture and runoff caused by enhanced evapotranspiration. However, these studies ignore important vegetation‐climate feedbacks that can partially offset such negative consequences. Here, we examine how revegetation affects water budgets in China's Loess Plateau, where the world's largest revegetation occurs. Despite increased evapotranspiration, long‐term observations exhibit robust increasing trends (2.76 mm yr⁻²) of surface water yield over a large swath (82.3%) of the Plateau since revegetation starts. This is mainly caused by increased regional precipitation that outweighs increases in evapotranspiration. Numerical experiments further reveal that the increased precipitation is largely driven by revegetation‐induced enhancement in land‐atmosphere interactions that greatly accelerate local moisture recycling. Our findings highlight the importance of considering vegetation‐climate feedbacks in assessing hydrological responses to large‐scale vegetation changes.

Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.

Reservoir hydropower offers a compelling combination of stability and flexibility services for modern water and power grids. However, its operating flexibility is poorly characterized in energy system planning, missing opportunities to cost-effectively uptake variable renewable energy (VRE) for a clean energy transition. In this study, we have developed a fully coupled reservoir operation and energy expansion model to quantify the economic and environmental benefits attained from adaptive hydropower operation in a high VRE future. Our case study of the China Southern Power Grid reveals that, in a 2050 net-zero grid, simply adapting hydropower operations to balance VRE can reduce 2018–2050 total system costs by 7% (that is, US$28.2 billion) and simultaneously save 123.8 km³ of water each year (that is, more than three times the reservoir capacity of the Three Gorges Dam). These vast, yet overlooked, cost- and water-saving potentials highlight the importance of incorporating balancing-oriented hydropower operation into future pathways to jointly decarbonize and secure power and water grids.