Increases in climate hazards and their impacts mark one of the major challenges of climate change. Situations in which hazards occur close enough to one another to result in amplified impacts, because systems are insufficiently resilient or because hazards themselves are made more severe, are of special concern. We consider projected changes in such compounding hazards using the Max Planck Institute Grand Ensemble under a moderate (RCP4.5) emissions scenario, which produces warming of about 2.25 °C between pre-industrial (1851–1880) and 2100. We find that extreme heat events occurring on three or more consecutive days increase in frequency by 100%–300%, and consecutive extreme precipitation events increase in most regions, nearly doubling for some. The chance of concurrent heat and drought leading to simultaneous maize failures in three or more breadbasket regions approximately doubles, while interannual wet-dry oscillations become at least 20% more likely across much of the subtropics. Our results highlight the importance of taking compounding climate extremes into account when looking at possible tipping points of socio-environmental systems.

Here, we evaluate basin‐scale evapotranspiration (ET) estimates for eleven major river basins in the contiguous United States against a water balance approach with Gravity Recovery and Climate Experiment (GRACE) satellite observations. The relatively precise measurements of large‐scale changes in water mass from GRACE are used to estimate the storage rate term in the terrestrial water budget and consequently provide an estimate, with propagated uncertainty, of basin‐aggregated ET from mass conservation. We apply GRACE‐based ET to two modeling systems (NLDAS‐2 and GLDAS‐2.1) comprised of five land surface models and three remote sensing‐based products (MOD16, PT‐JPL, and FLUXCOM) for 2003 to 2014. Both the land surface model‐based and remote sensing‐based ET are persistently lower than GRACE‐based ET in all eleven basins tested. We also find that interannual variability is greater for GRACE‐ET than the model and remote sensing products, and this is attributed to precipitation variability.

Efficiently managing agricultural irrigation is vital for food security today and into the future under climate change. Yet, evaluating agriculture’s hydrological impacts and strategies to reduce them remains challenging due to a lack of field-scale data on crop water consumption. Here, we develop a method to fill this gap using remote sensing and machine learning, and leverage it to assess water saving strategies in California’s Central Valley. We find that switching to lower water intensity crops can reduce consumption by up to 93%, but this requires adopting uncommon crop types. Northern counties have substantially lower irrigation efficiencies than southern counties, suggesting another potential source of water savings. Other practices that do not alter land cover can save up to 11% of water consumption. These results reveal diverse approaches for achieving sustainable water use, emphasizing the potential of sub-field scale crop water consumption maps to guide water management in California and beyond.