We propose a novel statistical learning approach for detection and attribution of climate change. The first part of the talk will focus on daily detection and show that we can now detect climate change from global weather for any single day since spring 2012. The second part of the talk will focus on attribution of climate change. We want the prediction of the external forcing of interest, e.g., anthropogenic forcing, to work well even under changes in the distribution of other external forcings, e.g., solar or volcanic forcings. We therefore formulate the optimization problem from a distributional robustness perspective, allowing us to do attribution.

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