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.

翻译：精准的气候预测对于气候适应与减缓至关重要，但地球系统模型仍存在巨大不确定性。尽管已有若干方法用于减少气候预测与反馈的离散度，但这些方法无法捕捉气候系统固有的非线性复杂性。通过采用迁移学习方法，我们证明机器学习能够最优地利用并融合地球系统模型模拟与历史观测数据，从而更精确地预测21世纪全球地表气温场。与现有最先进方法相比，我们实现了超过50%的不确定性降低。实证表明，这一新方法在提供更窄的预测不确定性的同时，还能给出更精确的平均气候预测——这正是气候适应领域迫切需要的。