Jhayron S. Pérez-Carrasquilla

Postdoctoral Scholar, UChicago Data Science Institute | AICE: AI for Climate | Earth System Prediction & Machine Learning

I develop and apply machine learning to improve subseasonal-to-seasonal forecasts and understand the physical processes that shape predictability and uncertainty.

Email Google Scholar GitHub ORCID LinkedIn

About

I am a Postdoctoral Scholar at the UChicago Data Science Institute within the AICE: AI for Climate Initiative, working at the intersection of AI and Earth system prediction. My research develops and applies machine learning to improve subseasonal-to-seasonal (S2S) forecasts, while using scientific insight to understand the physical processes that shape predictability and uncertainty.

I completed my PhD in Atmospheric and Oceanic Science at the University of Maryland. My work spans predictability across timescales, from subseasonal forecasting to longer-term climate variability and change, with particular interests in large-scale circulation, tropical variability, extreme events, and process-oriented climate science.

Across academic, public-sector, and applied research settings, I have worked on subseasonal prediction, air quality, extreme events, renewable-energy forecasting, and climate risk. I combine Earth system models, reanalyses, observations, and data-driven approaches—including tree-based models, deep learning, explainable AI, and causal discovery—to investigate both predictive skill and its physical basis.

I contribute to international efforts at the intersection of climate science and AI, including the WCRP Fresh Eyes on CMIP initiative and the AMS Committee on AI Applications to Environmental Science. I was also an ASP Graduate Visitor at NSF NCAR, where I studied large-scale circulation changes and associated extremes. Broadly, I am interested in building AI-enabled prediction systems that are scientifically grounded, interpretable, and useful for understanding a complex and changing Earth system.

I earned bachelor’s and master’s degrees from the Universidad Nacional de Colombia. Outside research, I enjoy sports, music, movies, and reading.

Research

Predictability across timescales

I study the sources and limits of atmospheric predictability, especially for North American weather regimes at subseasonal-to-seasonal lead times. This work uses machine learning, Earth system reanalyses, and coupled model output to diagnose when and why useful forecast information emerges.

Large-scale circulation and extremes

I analyze how large-scale circulation patterns vary across observations and models, how they shape extreme heat and other impacts, and how their behavior may change in a warming climate.

Causal and interpretable machine learning

I use explainable AI and causal discovery methods to connect predictive models with physical interpretation, including work on dynamical precursors of extreme events from gridded Earth system data.

Applied forecasting experience

Before and alongside my PhD, I worked on applied forecasting problems involving air quality, streamflow, meteorological variables, renewable-energy resources, and hurricane risk products for public and private-sector decision-making.

Subseasonal prediction Weather regimes Extreme events Machine learning Causal discovery Large ensembles Reanalysis Satellite and radar data

Publications

Selected experience

Postdoctoral Scholar
UChicago Data Science Institute, AICE: AI for Climate Initiative | Chicago, IL | 2026–Present
  • Developing interpretable generative AI frameworks for subseasonal-to-seasonal forecasting of extreme events.
  • Working on uncertainty quantification and reduction in regional climate projections.
PhD, Atmospheric and Oceanic Science
University of Maryland, Department of Atmospheric and Oceanic Science | 2022–2026
  • Developed machine-learning methods to diagnose sources of subseasonal predictability in North American weather regimes.
  • Analyzed weather regime variability and long-term changes using reanalysis data, climate model output, and large ensembles.
ASP Graduate Visitor
US NSF National Center for Atmospheric Research | Boulder, CO | 2024
  • Studied changes in North American weather regimes and associated surface extremes using Earth system model ensembles.
Research Scientist and Software Developer
Corporación Clima S.A.S. | Medellín, Colombia | 2020–2021
  • Developed machine-learning forecasts for meteorological, hydrological, energy-sector, and insurance-sector applications.
  • Built automated hurricane risk reports using National Weather Service model output.
Research Scientist
SIATA Early Warning System of the Aburrá Valley | Medellín, Colombia | 2018–2021
  • Developed machine-learning forecasts of air quality and analyzed drivers of convection and poor air quality.
  • Led atmospheric sampling campaigns and communicated scientific results with public-sector and community stakeholders.

Awards and honors

Connect

Contact: jhayron@uchicago.edu