| Summary: | 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.
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