Trok J.T. et al. “Large-scale circulation patterns regulate the response of extreme atmospheric river events to global warming.”
Trok, J.T., Barnes, E.A., Gordon, E.M., Davenport, F.V. & Diffenbaugh, N.S. “Extreme event attribution based on fractional shares of historical emissions.” Submitted to PNAS.
Fay R.L., Glidden C.K., Trok J.T., Diffenbaugh N.S., Ciota A.T., Mordecai E.A. “The impact of climate change on transmission season length: West Nile virus as a case study.” bioRxiv [Preprint]
Harris, M.J., Trok, J.T., Martel, K.S., Cordova, M.J.B., Diffenbaugh, N.S., Munayco, C.V., ... & Mordecai, E.A. (2026). "Extreme precipitation, exacerbated by anthropogenic climate change, drove Peru’s record-breaking 2023 dengue outbreak." One Earth. [Article Link]
Callahan, C.W., Trok, J.T., Wilson, A.J., Gould, C.F., Heft-Neal, S., Diffenbaugh, N.S., & Burke, M. "Increasing risk of mass human heat mortality if historical weather patterns recur." Nature Climate Change, 1-7. (2025). [Article Link]
Callahan, C., Trok, J.T., Wilson, A., Gould, C., Heft-Neal, S., Burke, M., & Diffenbaugh, N.S. "Quantifying the contributions of climate change and adaptation to mortality from unprecedented extreme heat events." Proceedings of the National Academy of Sciences, 122, e2503577122 (2025). [Article Link]
Trok, J.T., Barnes, E.A., Davenport, F.V. & Diffenbaugh, N.S. "Machine learning–based extreme event attribution." Sci. Adv. 10, eadl3242 (2024). [Article Link]
Rastogi, D., Trok, J.T., Depsky, N., Monier, E. & Jones, A.D. "Historical evaluation and future projections of compound heatwave and drought extremes over the conterminous United States in CMIP6." Environ. Res. Lett. 19, (2023) [Article Link]
Trok, J.T., Davenport, F.V., Barnes, E.A., Diffenbaugh, N.S. "Using machine learning with partial dependence analysis to investigate coupling between soil moisture and near‐surface temperature." Journal of Geophysical Research: Atmospheres 128 (2023) [Article Link]
Conference on Attribution Science and Climate Law 2026, Columbia University, NY (upcoming)
[Poster] Extreme event attribution based on fractional shares of historical emissions
Doerr School of Sustainability Research Review 2026, Stanford, CA
[Oral] Extreme event attribution based on fractional shares of historical emissions
Water in the West Conference 2026, Stanford, CA
[Invited] Large-scale circulation patterns regulate the response of extreme atmospheric river
events to global warming
American Geophysical Union Fall Meeting 2025, New Orleans, LA
[Oral] Large-scale circulation patterns regulate the response of extreme atmospheric river
events to global warming
IPSL’s Extremes: Statistics, Impacts and Regionalization Research Group 2025
[Invited] Using machine learning to quantify uncertainty in extreme event attribution
American Geophysical Union Fall Meeting 2024, Washington D.C.
[Oral] Quantifying uncertainty in extreme event attribution using neural networks
California E.P.A.’s Office of Environmental Health Hazard Assessment 2024
[Invited] ML-based extreme event attribution: implications for California
Climate Resolve’s Integrated Climate Committee on Extreme Heat Meeting 2024
[Invited] ML-based extreme event attribution: implications for California
American Geophysical Union Fall Meeting 2023, San Francisco, CA
[Oral] Machine learning-based extreme event attribution
American Geophysical Union Fall Meeting 2022, Chicago, IL
[Oral] Using machine learning with partial dependence analysis to investigate coupling
between soil moisture and near‐surface temperature
Stanford University 2021 – present
PhD, Earth System Science (expected 2027)
University of California–Davis 2017 – 2021
BS, Atmospheric Sciences
BS, Applied Mathematics
American Meteorological Society Graduate Fellowship 2021 – 2022
Atmospheric Sciences Departmental Award, University of California – Davis 2021
Regents Scholar, University of California – Davis 2017 – 2021