Storyline attribution of human influence on a record-breaking spatially compounding flood-heat event.
Jun Wang, Yang Chen, Simon F B Tett, Dáithí Stone, Ji Nie, Jinming Feng, Zhongwei Yan, Panmao Zhai, Quansheng Ge
Author Information
Jun Wang: The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. ORCID
Yang Chen: State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China. ORCID
Simon F B Tett: School of GeoSciences, The University of Edinburgh, Edinburgh, UK. ORCID
Dáithí Stone: National Institute of Water and Atmospheric Research, Wellington, Aotearoa, New Zealand.
Ji Nie: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China. ORCID
Jinming Feng: Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. ORCID
Zhongwei Yan: Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. ORCID
Panmao Zhai: State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China. ORCID
Quansheng Ge: The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. ORCID
Attribution of compound events informs preparedness for emerging hazards with disproportionate impacts. However, the task remains challenging because space-time interactions among extremes and uncertain dynamic changes are not satisfactorily addressed in the well-established attribution framework. For attributing the 2020 record-breaking spatially compounding flood-heat event in China, we conduct a storyline attribution analysis by designing simulation experiments via a weather forecast model, quantifying component-based attributable changes, and comparing with historical flow analogs. We quantify that given the large-scale circulation, anthropogenic influence to date has exacerbated the extreme Mei-yu rainfall in the mid-lower reaches of the Yangtze River during June-July 2020 by ~6.5% and warmed the co-occurring seasonal extreme heat in South China by ~1°C. Our projections show a further intensification of the compound event by the end of this century, with moderate emissions making the rainfall totals ~14% larger and the season ~2.1°C warmer in South China than the 2020 status.
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