Accurate climate projections are required for climate adaptation and mitigation. Earth system model simulations, used to project climate change, inherently make approximations in their representation of small-scale physical processes, such as clouds, that are at the root of the uncertainties in global mean temperature's response to increased greenhouse gas concentrations. Several approaches have been developed to use historical observations to constrain future projections and reduce uncertainties in climate projections and climate 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, in particular Deep Neural Networks, can be used to optimally leverage and merge the knowledge gained from Earth system model simulations and historical observations to more accurately project global surface temperature fields in the 21st century. For the Shared Socioeconomic Pathways (SSPs) 2-4.5, 3-7.0 and 5-8.5, we refine regional estimates and the global projection of the average global temperature in 2081-2098 (with respect to the period 1850-1900) to 2.73{\deg}C (2.44-3.11{\deg}C), 3.92{\deg}C (3.5-4.47{\deg}C) and 4.53{\deg}C (3.69-5.5{\deg}C), respectively, compared to the unconstrained 2.7{\deg}C (1.65-3.8{\deg}C), 3.71{\deg}C (2.56-4.97{\deg}C) and 4.47{\deg}C (2.95-6.02{\deg}C). Our findings show that the 1.5{\deg}C threshold of the Paris' agreement will be crossed in 2031 (2028-2034) for SSP2-4.5, in 2029 (2027-2031) for SSP3-7.0 and in 2028 (2025-2031) for SSP5-8.5. Similarly, the 2{\deg}C threshold will be exceeded in 2051 (2045-2059), 2044 (2040-2047) and 2042 (2038-2047) respectively. Our new method provides more accurate climate projections urgently required for climate adaptation.

翻译：准确的气候预测对于气候适应和减缓至关重要。用于预测气候变化的地球系统模型模拟，在其对小尺度物理过程（如云）的表征中固有地引入了近似，而这些过程正是全球平均温度对温室气体浓度增加响应的不确定性根源。已有多种方法利用历史观测数据来约束未来预测，以减少气候预测和气候反馈中的不确定性。然而，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（特别是深度神经网络）能够最优地利用并融合从地球系统模型模拟和历史观测中获得的知识，从而更准确地预测21世纪全球地表温度场。对于共享社会经济路径（SSPs）2-4.5、3-7.0和5-8.5，我们将2081-2098年全球平均温度的预估结果（相对于1850-1900年）分别细化为2.73°C（2.44-3.11°C）、3.92°C（3.5-4.47°C）和4.53°C（3.69-5.5°C），而未经约束的预估结果分别为2.7°C（1.65-3.8°C）、3.71°C（2.56-4.97°C）和4.47°C（2.95-6.02°C）。我们的研究结果表明，根据《巴黎协定》的1.5°C阈值，SSP2-4.5情景将在2031年（2028-2034年）被跨越，SSP3-7.0情景在2029年（2027-2031年），SSP5-8.5情景在2028年（2025-2031年）。同样，2°C阈值将分别在2051年（2045-2059年）、2044年（2040-2047年）和2042年（2038-2047年）被逾越。我们的新方法提供了气候适应所迫切需要的更准确的气候预测。