Jingfang Fan
Professor, Director, Ph.D. Supervisor · School of Systems Science, Beijing Normal University
Professor and Dean of the School of Systems Science at Beijing Normal University; Guest Professor at the Potsdam Institute for Climate Impact Research; Vice Chair of the Chinese Society of Systems Engineering; and Member of the China Society for Industrial and Applied Mathematics.
He has been selected for the National Science Fund for Distinguished Young Scholars (Category A) and the Youth Program of the Overseas High-Level Talent Program of the Organization Department of the CPC Central Committee. His honors include the Young Scientist and Technologist Award for Systems Science and Systems Engineering (2024, the highest honor for young researchers in China's systems engineering community), the Tsinghua University-Inspur Young Talent Award for Computational Earth Science (2025), and a nomination for the Powerful Nation Young Scientist Award (2023).
As first or corresponding author, he has published more than 40 papers in journals including Nat. Phys., Nat. Clim. Change, Nat. Mach. Intell., PNAS, and PRL. He serves as a Guest Editorial Board Member of Natl. Sci. Rev. and as an editorial board member or young editorial board member for several journals in China and abroad, including JPhys Complexity.
His research focuses on the theory and applications of complex systems.
Research Interests
Recent Highlights
Community structure-regulation coupling reveals optimal information diffusion
It reveals three macroscopic states of information diffusion under the combined effect of structure and regulatory parameters, along with their critical phase diagram. It identifies the optimal control domain for containing propagation at the minimum intervention cost, and uncovers a non-monotonic dependence between the optimal intervention cost and the degree of community structure in the system.
Read more →
Learning the coupled dynamics of global climate modes
Introduces UniCM, a unified deep model that learns localized dynamics and global couplings directly from data to improve unified forecasting across major climate modes, published in Nature Machine Intelligence.
Enhancing the Predictability Limits of ENSO with Physics-Guided Deep Echo State Networks
A real-time ENSO forecasting workflow based on ORAS5 and DESN, showing climate-mode construction, ensemble training, and Niño3.4 predictions for 2026-2027.
Self-Organized Criticality in Atmospheric Rivers
Starting from the theory of critical dynamics in statistical physics, it reveals that atmospheric rivers, as key water vapor transport structures in the Earth climate system, have the essential property of self-organized criticality.
Tropical monsoon rainfall can be predicted with lead times up to 10 months
Presents methods and evidence showing tropical monsoon rainfall can be predicted with lead times up to 10 months, published in Communications Earth & Environment.
News
Professor Jingfang Fan and her team have published their findings on critical dynamics in complex systems in Nature Communications. (opens in a new tab)
Jingfang Fan's team proposes UniCM, a unified prediction model for global climate modes (opens in a new tab)
Jingfang Fan's team reveals the self-organized critical dynamics of atmospheric rivers in Physical Review Letters (opens in a new tab)
Jingfang Fan's team develops a physics-guided machine-learning approach to improve long-term ENSO predictability (opens in a new tab)
Recent Publications
Contact & Collaboration
- Address
- Room 9911, Jingshi Building, Beijing Normal University
- Postal code
- 100875
- Phone
- +86 10 58800116
- Graduate admissions
- https://sss.bnu.edu.cn/yjszsztw/