Extreme heat poses a growing public risk, yet understanding real-time, population-level human response remains a major challenge. This study leverages Google Trends search activity (2016–2024) across 30 major US metropolitan areas to quantify digital responses to heat. Using generalized additive models, two key response metrics are identified: a temperature ‘threshold’ that triggers public concern and a ‘slope difference’ that measures the intensity of the subsequent reaction. The analysis first establishes that a city’s median climate primarily determines its baseline threshold, while the variability of climate governs the slope difference, both reflecting long-term adaptation to local conditions. After accounting for these climatic effects, the study finds that socioeconomic factors explain a significant portion of the remaining variance in digital heat responsiveness. Specifically, the climate-adjusted threshold is higher in cities with greater social vulnerability, indicating a dangerous delay in awareness. This interplay creates five distinct urban typologies, revealing that temperate coastal cities exhibit a highly reactive pattern while arid inland cities show a response buffered by modern infrastructure. These findings uncover a critical disconnect wherein the populations most at risk are often the last to digitally engage with the threat. This digital proxy provides a new, near real-time method for monitoring public risk perception, offering a vital resource for developing more equitable public health interventions against the escalating threat of extreme heat.

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