As heatwaves increase in both frequency and intensity globally, the need to develop tools to predict the human impact and develop a more comprehensive understanding of the impact mechanism at a population level is becoming more urgent. Our study provides a taxonomy of heatwaves based on identifying sub-threshold lethal heatwaves through physiological adaptation and vulnerability. We use a classification algorithm applied to a lethal heatwave dataset, comprising 125,411 events where the temperature exceeded the 90th percentile across 140 cities, with combined meteorology and sociodemographic inputs to label these events. The accuracy of our model outperforms classification that relies on wet bulb temperature thresholds with a factor of 11 improvement in imbalanced classification performance. Furthermore, we find that the majority of lethal heatwaves within our dataset occur below high wet bulb temperature thresholds and that accurate predictions for heatwave mortality can be obtained by combining thermo-temporal differentials and population health metrics instead of absolute climatic conditions. We thus propose classifying heatwaves as either: Shock Heatwaves, where aggressive thermo-temporal differentials from a local acclimation point trigger adverse stress effects, particularly among the vulnerable; or Threshold Heatwaves, where high temperature and humidity conditions do exceed the ability to dissipate heat effectively.
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