  Accurate and precise climate projections are required for climate adaptation
and mitigation, but Earth system models still exhibit great uncertainties.
Several approaches have been developed to reduce the spread of climate
projections and feedbacks, yet those methods cannot capture the non-linear
complexity inherent in the climate system. Using a Transfer Learning approach,
we show that Machine Learning can be used to optimally leverage and merge the
knowledge gained from Earth system models simulations and historical
observations to more accurately project global surface air temperature fields
in the 21st century. We reach an uncertainty reduction of more than 50% with
respect to state-of-the-art approaches. We give evidence that our novel method
provides narrower projection uncertainty together with more accurate mean
climate projections, urgently required for climate adaptation.
