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机器学习驱动的极端事件归因 摘要 The observed increase in extreme weather has prompted recent
methodological advances in extreme event attribution. We propose a machine
learning–based approach that uses convolutional neural networks to create
dynamically consistent counterfactual versions of historical extreme events
under different levels of global mean temperature (GMT). We apply this
technique to one recent extreme heat event (southcentral North America 2023)
and several historical events that have been previously analyzed using established
attribution methods. We estimate that temperatures during the southcentral
North America event were 1.18° to 1.42°C warmer because of global warming and
that similar events will occur 0.14 to 0.60 times per year at 2.0°C above
preindustrial levels of GMT. Additionally, we find that the learned
relationships between daily temperature and GMT are influenced by the
seasonality of the forced temperature response and the daily meteo
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