On the morning of September 1, the "AI and Climate Change Lecture Series" was held in the Lecture Hall of Tsinghua University’s Ziqiang Science and Technology Building. The event was co-sponsored by Chinese Science Bulletin and Science Bulletin, and co-hosted by the Department of Earth System Science (DESS) at Tsinghua University and the Institutes of Science and Development of the Chinese Academy of Sciences. Centering on the theme of “Artificial Intelligence and Climate Change,” the event brought together cutting-edge scientific research and national strategies, aiming to foster in-depth interdisciplinary dialogue and explore new pathways for AI to advance climate science. It featured keynote reports and roundtable discussions, adopting a hybrid format with both offline participation and live online streaming. Nearly 100 faculty members and students attended in person, while over 74,000 viewers joined online.

Chen Deliang, foreign academician of the Chinese Academy of Sciences and Xinghua Distinguished Chair Professor of Tsinghua University, and Chen Fahu, academician of the Chinese Academy of Sciences and researcher of the Institute of Tibetan Plateau Research of the Chinese Academy of Sciences, jointly served as conveners. Distinguished guests attending the event included Du Xiangwan, an academician of the Chinese Academy of Engineering and its former Vice President; Guo Huadong, academician of the Chinese Academy of Sciences and researcher of the Aerospace Information Research Institute of the Chinese Academy of Sciences; Mu Bin, professor of the School of Computer Science and Technology of Tongji University; Lu Bo, Director of the Climate Change Impact and Adaptation Division of the National Climate Center and Vice President of the Xiong’an Meteorological Artificial Intelligence Innovation Research Institute; and An Rui, Deputy General Manager of Science China Press and Director of the Editorial Office of Chinese Science Bulletin. The event was hosted by Academician Chen Fahu.

Event Venue

Keynote Speakers: Huang Xiaomeng, Li Hao, and Tan Xianchun (from left to right)

Professor Huang Xiaomeng from Tsinghua DESS delivered a report titled "Research Progress on Multi-Scale Ocean-Atmosphere Sub-seasonal Forecasting Large Models", introducing the latest breakthroughs in the field of forecasting large models. Li Hao, researcher of Fudan University and Head of Earth Sciences at the Shanghai Academy of AI for Science (SAIS), presented "Technical Challenges of the Fuxi Meteorological Model in Climate Prediction", analyzing the development direction of the new-generation intelligent meteorological models. Tan Xianchun, researcher from the Carbon Neutrality Strategy Research Center of the Institutes of Science and Development of the Chinese Academy of Sciences, shared insights from the perspective of "Artificial Intelligence Promoting Climate Change Research and Action: Opportunities, Challenges and Future Prospects", expounding the strategic value of AI in addressing climate change.

Chen Deliang Hosting the Roundtable Discussion

Roundtable Speakers: Guo Huadong, Chen Fahu, Mu Bin, and Lu Bo (from top to bottom, left to right)

Chaired by Professor Chen Deliang, the roundtable discussion centered on “Cutting-Edge Breakthroughs and Simulation Challenges in Climate Science, and AI-Assisted Policy Pathways for Climate Change.” Experts delved into the application of AI in advancing climate modeling, enhancing predictive capabilities, and designing policy pathways, inspiring new approaches for interdisciplinary collaboration and policy practice.

The "AI and Climate Change Lecture Series" serves not only as a significant academic forum but also as a vital platform for aligning technological innovation with national strategies. Moving forward, Tsinghua University will leverage its multidisciplinary strengths, foster greater collaboration in scientific research both domestically and internationally, and further integrate artificial intelligence with Earth system science to support global climate response efforts and national strategic goals.