Weather extremes have widespread harmful impacts on ecosystems and human communities with more deaths and economic losses from flash floods than any other severe weather-related hazards. Flash floods attributed to storm runoff extremes are projected to become more frequent and damaging globally due to a warming climate and anthropogenic changes, but previous studies have not examined the response of these storm runoff extremes to naturally and anthropogenically driven changes in surface temperature and atmospheric moisture content. Here we show that storm runoff extremes increase in most regions at rates higher than suggested by Clausius-Clapeyron scaling, which are systematically close to or exceed those of precipitation extremes over most regions of the globe, accompanied by large spatial and decadal variability. These results suggest that current projected response of storm runoff extremes to climate and anthropogenic changes may be underestimated, posing large threats for ecosystem and community resilience under future warming conditions.

Estimates of change in global land evapotranspiration (ET) are necessary for understanding the terrestrial hydrological cycle under changing environments. However, large uncertainties still exist in our estimates, mostly related to the uncertainties in upscaling in situ observations to large scale under non-stationary surface conditions. Here, we use machine learning models, artificial neural network and random forest informed by ground observations and atmospheric boundary layer theory, to retrieve consistent global long-term latent heat flux (ET in energy units) and sensible heat flux over recent decades. This study demonstrates that recent global land ET has increased significantly and that the main driver for the increased ET is increasing temperature. Moreover, the results suggest that the increasing ET is mostly in humid regions such as the tropics. These observation-driven findings are consistent with the idea that ET would increase with climate warming. Our study has important implications in providing constraints for ET and in understanding terrestrial water cycles in changing environments.

Evapotranspiration (ET) accompanied by water and heat transport in the hydrological cycle is a key component in regulating surface aridity. Existing studies documenting changes in surface aridity have typically estimated ET using semi-empirical equations or parameterizations of land surface processes, which are based on the assumption that the parameters in the equation are stationary. However, plant physiological effects and its responses to a changing environment are dynamically modifying ET, thereby challenging this assumption and limiting the estimation of long-term ET. In this study, the latent heat flux (ET in energy units) and sensible heat flux were retrieved for recent decades on a global scale using a machine learning approach and driven by ground observations from flux towers and weather stations. This study resulted in several findings; for example, the evaporative fraction (EF) – the ratio of latent heat flux to available surface energy – exhibited a relatively decreasing trend on fractional land surfaces. In particular, the decrease in EF was accompanied by an increase in long-term runoff as assessed by precipitation (P) minus ET, accounting for 27.06 % of the global land areas. The signs are indicative of reduced surface conductance, which further emphasizes that surface vegetation has major impacts in regulating water and energy cycles, as well as aridity variability.