Bio

I am currently a Postdoctoral Scholar in the Departments of Medicine and Health Policy at Stanford University, after earning a PhD in atmospheric science from Purdue University. My research interests lie at the intersection of climate change—particularly extreme heat—and human society. I aim to advance our understanding of the physical mechanisms, cascading impacts, and the effectiveness of potential mitigation strategies for human heat stress. My PhD research focused on how land-atmosphere interactions modulate heat stress, as well as the economic and energy impacts of increasing heat stress in the context of climate change. My postdoctoral research at Stanford evaluates the impact of heat stress on public health, especially human fertility, in low- and middle-income countries. My methodological areas of expertise include climate modeling, human biophysics modeling, and econometric modeling, which I am further developing at Stanford.

Honors & Awards

  • NCAR ASP Summer Program NSF funded, NCAR (2023)
  • June L. and Tan (Mark) Sun Chen Research Scholarship, Purdue University (2023)
  • NASA Future Investigators in Earth and Space Science Technology, NASA (2022)
  • Henry Silver Graduate Scholarship, Purdue University (2022)

Stanford Advisors

Contact

  • Academic
    qqkong@stanford.edu
    University - Scholar Department: School of Medicine Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • A Linear Sensitivity Framework to Understand the Drivers of the Wet-Bulb Globe Temperature Changes JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES Kong, Q., Huber, M. 2025; 130 (5)

    View details for DOI 10.1029/2024JD042195

    View details for Web of Science ID 001432871100001

  • El Niño Enhances Exposure to Humid Heat Extremes With Regionally Varying Impacts During Eastern Versus Central Pacific Events GEOPHYSICAL RESEARCH LETTERS Menzo, Z. M., Karamperidou, C., Kong, Q., Huber, M. 2025; 52 (4)

    View details for DOI 10.1029/2024GL112387

    View details for Web of Science ID 001420419200001

  • A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics SCIENTIFIC DATA Kong, Q., Huber, M. 2025; 12 (1): 246

    Abstract

    Increasing heat stress with climate change will threaten human health and cause broad social and economic impacts. The evaluation of such impacts depends on a reliable dataset of heat stress projection. Here we present a global dataset of the future projection of dry-bulb, wet-bulb and wet-bulb globe temperature under 1-4°C of global warming levels compared with the preindustrial era using output from 16 CMIP6 global climate models (GCMs). The dataset was bias-corrected against ERA5 reanalysis by adding the GCM-simulated climate change signal onto ERA5 baseline (1950-1976) at 3-hourly frequency. The resulting datasets are provided at fine spatial (0.25° × 0.25°) and temporal (3-hourly) resolution. We validate the bias-correction approach and demonstrate that it substantially improves the GCMs' ability to reproduce both the annual average and entire range of quantiles for all metrics within an ERA5 reference climate state. We expect the dataset to benefit future work on estimating projected changes in both mean and extreme heat stress and assessing consequential health and social-economic impacts.

    View details for DOI 10.1038/s41597-025-04527-6

    View details for Web of Science ID 001421222600012

    View details for PubMedID 39939321

    View details for PubMedCentralID PMC11821900

  • Mortality impacts of the most extreme heat events NATURE REVIEWS EARTH & ENVIRONMENT Matthews, T., Raymond, C., Foster, J., Baldwin, J. W., Ivanovich, C., Kong, Q., Kinney, P., Horton, R. M. 2025

    View details for DOI 10.1038/s43017-024-00635-w

    View details for Web of Science ID 001414216900001

  • A New, Zero-Iteration Analytic Implementation of Wet-Bulb Globe Temperature: Development, Validation, and Comparison With Other Methods GEOHEALTH Kong, Q., Huber, M. 2024; 8 (10): e2024GH001068

    Abstract

    Wet-bulb globe temperature (WBGT)-a standard measure for workplace heat stress regulation-incorporates the complex, nonlinear interaction among temperature, humidity, wind and radiation. This complexity requires WBGT to be calculated iteratively following the recommended approach developed by Liljegren and colleagues. The need for iteration has limited the wide application of Liljegren's approach, and stimulated various simplified WBGT approximations that do not require iteration but are potentially seriously biased. By carefully examining the self-nonlinearities in Liljegren's model, we develop a zero-iteration analytic approximation of WBGT while maintaining sufficient accuracy and the physical basis of the original model. The new approximation slightly deviates from Liljegren's full model-by less than 1°C in 99% cases over 93% of global land area. The annual mean and 75%-99% percentiles of WBGT are also well represented with biases within ± 0.5 °C globally. This approximation is clearly more accurate than other commonly used WBGT approximations. Physical intuition can be developed on the processes controlling WBGT variations from an energy balance perspective. This may provide a basis for applying WBGT to understanding the physical control of heat stress.

    View details for DOI 10.1029/2024GH001068

    View details for Web of Science ID 001383292800001

    View details for PubMedID 39350796

    View details for PubMedCentralID PMC11439757

https://orcid.org/0000-0002-4593-3643