School of Energy and Environment

Prof. Xueke LI

PhD in Geography, University of Connecticut
Assistant Professor

Prof. Xueke Li is a climate scientist. Her research spans climate variability and extremes, from mid-latitude weather extremes to polar sea ice loss, using remote sensing, machine learning, and Earth system modeling to better understand, predict, and manage climate risks in a rapidly changing world. She received her Ph.D. in Geography from the University of Connecticut and subsequently worked as a Postdoctoral Research Associate with Dr. Amanda Lynch at Brown University. Prior to joining City University of Hong Kong, she was a Research Associate with Dr. Michael Mann in the Department of Earth and Environmental Science and the Penn Center for Science, Sustainability and the Media (PCSSM) at the University of Pennsylvania. Prof. Li has authored and co-authored more than 70 peer-reviewed publications, including first-author papers in leading journals such as the Proceedings of the National Academy of Sciences (PNAS). Her work has been extensively featured by media outlets, including The Guardian, The Associated Press, and The Sunday Times. She currently serves as an Associate Editor of JGR: Machine Learning and Computation and Physics and Chemistry of the Earth, Parts A/B/C, and as an Editorial Board Member of several journals, including Scientific Reports.

Research Interests

Prof. Li’s research seeks to advance understanding of climate variability and extremes, their societal and environmental impacts, and the risks they pose in a changing climate. By integrating big data, remote sensing, machine learning, and Earth system modeling, her work aims to improve the prediction of extremes and translate scientific understanding into actionable risk assessments that support effective adaptation and resilience-building strategies. Current research themes include:

  • Physical processes and dynamics underlying persistent warm-season weather extremes in a changing climate
  • Roles of ENSO, Arctic amplification, and land–atmosphere interactions in the predictability of extremes
  • Compound extremes and implications for urban and coastal resilience
  • Interaction of Arctic atmosphere-ice-ocean variability and marine accessibility under climate change
  • Climate risk assessment, sustainable shipping, and green maritime transitions
  • Water resources, drought, and land–atmosphere feedbacks under climate change
  • Remote sensing and machine learning for climate, sea ice, air quality, and hydrology

 

Position(s) Available

PhD position in climate and weather extremes and AI

The Computational Climate Extremes and Impacts Lab (C-CEIL) at the City University of Hong Kong (CityUHK) invites applications for a fully funded PhD position studying the physical processes and dynamics underlying persistent warm-season weather extremes in a changing climate, as well as the application of artificial intelligence (AI) to better understand, predict, and address climate and weather extremes. 

We seek highly motivated candidates with strong interests in one or more of the following areas: atmospheric dynamics, climate variability and extremes, Arctic amplification, land-atmosphere interactions, and climate change impacts on extreme weather events. Candidates interested in applying AI/ML to advance the understanding and prediction of climate and weather extremes are particularly encouraged to apply. 

Applicants should hold a B.S. or M.S. degree in atmospheric science, climate science, meteorology, Earth system science, environmental science, geography, mathematics, physics, computer science, or a closely related field. Candidates should possess strong quantitative and analytical skills. Prior experience with programming (Python, R, NCL, Bash, or similar), climate modeling, analysis of large observational and reanalysis datasets, numerical model output, remote sensing, AI/ML, or other data-driven methods to climate and Earth system science is highly desirable. 

The position is expected to commence in Fall 2027. Interested applicants are encouraged to email Dr. Xueke Li (xuekeli@cityu.edu.hk) with a brief introduction, research interests, and CV before submitting a formal application through the CityUHK portal (https://www.cityu.edu.hk/pg/research-degree-programmes/apply-now). Applications will be reviewed on a rolling basis until the position is filled.

City University of Hong Kong is an equal opportunity employer and we are committed to the principle of diversity. We encourage applications from all qualified candidates, especially applicants who will enhance the diversity of our staff.

Selected Publications

  1. Li, X., M. E. Mann, M. F. Wehner, and S. Christiansen (2025), Increased frequency of planetary wave resonance events over the past half-century, Proceedings of the National Academy of Sciences, 122(25), e2504482122. doi: 10.1073/pnas.2504482122. 
  2. Chen, K., X. Li, M. M. Weaver, S. A. Christiansen, A. L. Horton, and M. E. Mann (2025), The intensification of the strongest nor’easters, Proceedings of the National Academy of Sciences, 122(29), e2510029122. doi: 10.1073/pnas.2510029122. 
  3. Gupta, M., H. Reagan, Y. Koo, S. M. T. Chua, X. Li, and P. Heil (2025), Inferring the seasonality of sea ice floes in the Weddell Sea using ICESat-2. The Cryosphere, 19(3), 1241-1257. doi: 10.5194/tc-19-1241-2025.
  4. Li, X., M. E. Mann, M. F. Wehner, S. Rahmstorf, S. Petri, S. Christiansen, and J. Carrillo (2024), Role of atmospheric resonance and land-atmosphere feedbacks as a precursor to the June 2021 Pacific Northwest Heat Dome event, Proceedings of the National Academy of Sciences, 121(4), e2315330121. doi: 10.1073/pnas.2315330121. 
  5. Guimarães, S. O., M. E. Mann, S. Rahmstorf, S. Petri, B. A. Steinman, D. J. Brouillette, S. Christiansen, and X. Li (2024), Increased projected changes in quasi-resonant amplification and persistent summer weather extremes in the latest multimodel climate projections, Scientific Reports, 14(1), 21991. doi: 10.1038/s41598-024-72787-0. 
  6. Li, X., and A. H. Lynch (2024), Projections for Arctic marine accessibility: risk under climate change. Ocean and Coastal Law Journal, 29(2), 353. Available at: https://digitalcommons.mainelaw.maine.edu/oclj/vol29/iss2/9.
  7. Li, X., and A. H. Lynch (2023), New insights into projected Arctic sea road: operational risks, economic values, and policy implications, Climatic Change, 176(4), 30. doi: 10.1007/s10584-023-03505-4. 
  8. Liu, K., X. Li, S. Wang, and G. Zhou (2023), Past and future adverse response of terrestrial water storages to increased vegetation growth in drylands. npj Climate and Atmospheric Science, 6(1), 113. doi: 10.1038/s41612-023-00437-9. 
  9. Goldstein, M. A., A. H. Lynch, X. Li, and C. H. Norchi (2022), Sanctions or sea ice: Costs of closing the Northern Sea Route, Finance Research Letters, 50, 103257. doi: 10.1016/j.frl.2022.103257. 
  10. Lynch, A. H., C. H. Norchi, and X. Li (2022), The interaction of ice and law in Arctic marine accessibility, Proceedings of the National Academy of Sciences, 119(26), e2202720119. doi:10.1073/pnas.2202720119. 
  11. Liu, K., X. Li, S. Wang, and X. Gao (2022), Assessing the effects of urban green landscape on urban thermal environment dynamic in a semiarid city by integrated use of airborne data, satellite imagery and land surface model, International Journal of Applied Earth Observation and Geoinformation, 107, 102674. doi: 10.1016/j.jag.2021.102674.
  12. Li, X., A. H. Lynch, D. A. Bailey, S. R. Stephenson, and S. Veland (2021), The impact of black carbon emissions from projected Arctic shipping on regional ice transport, Climate Dynamics, 57(9), 2453-2466. doi: 10.1007/s00382-021-05814-9.
  13. Li, X., K. Liu, and J. Tian (2021), Variability, predictability, and uncertainty in global aerosols inferred from gap-filled satellite observations and an econometric modeling approach, Remote Sensing of Environment, 261, 112501. doi: 10.1016/j.rse.2021.112501.
  14. Li, X., S. R. Stephenson, A. H. Lynch, M. A. Goldstein, D. A. Bailey, and S. Veland (2021), Arctic shipping guidance from the CMIP6 ensemble on operational and infrastructural timescales, Climatic Change, 167(1), 23. doi: 10.1007/s10584-021-03172-3.
  15. Liu, K., X. Li, and S. Wang (2021), Characterizing the spatiotemporal response of runoff to impervious surface dynamics across three highly urbanized cities in southern China from 2000 to 2017, International Journal of Applied Earth Observation and Geoinformation, 100, 102331. doi: 10.1016/j.jag.2021.102331.
  16. Li, X., A. Seth, C. Zhang, R. Feng, X. Long, W. Li, and K. Liu (2020), Evaluation of WRF-CMAQ simulated climatological mean and extremes of fine particulate matter of the United States and its correlation with climate extremes, Atmospheric Environment, 222, 117181. doi: 10.1016/j.atmosenv.2019.117181.
  17. Li, X., C. Zhang, B. Zhang, and K. Liu (2019), A comparative time series analysis and modeling of aerosols in the contiguous United States and China, Science of The Total Environment, 690, 799-811. doi: 10.1016/j.scitotenv.2019.07.072.
  18. Li, X., C. Zhang, W. Li, R. O. Anyah, and J. Tian (2019), Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation, Journal of Cleaner Production, 223, 238-251. doi: 10.1016/j.jclepro.2019.03.121.
  19. Li, X., C. Zhang, W. Li, and K. Liu (2017), Evaluating the use of DMSP/OLS nighttime light imagery in predicting PM2.5 concentrations in the Northeastern United States, Remote Sensing, 9(6), 620. doi: 10.3390/rs9060620.
  20. Li, X., T. Wu, K. Liu, Y. Li, and L. Zhang (2016), Evaluation of the Chinese fine spatial resolution hyperspectral satellite TianGong-1 in urban land-cover classification, Remote Sensing, 8(5), 438. doi:10.3390/rs8050438. 

LiXueke_resize_b
Contact Information
Office: YEUNG-G5713
Phone: +(852)-3442-6791
Fax: +(852)-3442-0688
Email: xuekeli@cityu.edu.hk
Web: Computational Climate Extremes and Impacts Lab (C-CEIL)
Google Scholar:
 https://scholar.google.com/citations?hl=en&user=dCP1IOUAAAAJ&view_op=list_works&sortby=pubdate
Research Interests
  • Climate change, planetary waves, and weather extremes
  • Improved predictability of extremes across scales
  • Compound extremes and urban–coastal resilience
  • Arctic climate change, marine accessibility, and sustainable shipping
  • Water resources, land–atmosphere interactions, and water–carbon–food security
  • Remote sensing, machine learning, and Earth system modeling

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