Abstract. Evapotranspiration (ET) accompanied by water and heat transport in the hydrological cycle is a key component in regulating surface aridity. Existing studies on 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 response 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 machine learning approach and driven by ground-based observations from flux towers and weather stations. The study resulted in several findings, namely that 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 were indicative of reduced surface conductance, which further emphasized that land-surface vegetation has major impacts on regulating the water and energy cycles, as well as aridity variability.