  Estimating historical evapotranspiration (ET) is essential for understanding
the effects of climate change and human activities on the water cycle. This
study used historical weather station data to reconstruct ET trends over the
past 300 years with machine learning. A Random Forest model, trained on
FLUXNET2015 flux stations' monthly data using precipitation, temperature,
aridity index, and rooting depth as predictors, achieved an R2 of 0.66 and a
KGE of 0.76 through 10-fold cross-validation. Applied to 5267 weather stations,
the model produced monthly ET data showing a general increase in global ET from
1700 to the present, with a notable acceleration after 1900 due to warming.
Regional differences were observed, with higher ET increases in mid-to-high
latitudes of the Northern Hemisphere and decreases in some mid-to-low latitudes
and the Southern Hemisphere. In drylands, ET and temperature were weakly
correlated, while in humid areas, the correlation was much higher. The
correlation between ET and precipitation has remained stable over the
centuries. This study extends the ET data time span, providing valuable
insights into long-term historical ET trends and their drivers, aiding in
reassessing the impact of historical climate change and human activities on the
water cycle and supporting future climate adaptation strategies.
