<p>Heatwaves are becoming more intense and frequent as global temperatures rise, affecting vulnerable populations, particularly in low-income communities. Addressing the impacts of heatwaves requires high-resolution data to assess their influence on labour productivity, public health, and climate risk. We introduce the Comprehensive Heat Indices (CHI) dataset, a high-resolution (0.1° × 0.1°) hourly dataset from 1950 to 2024, derived from the ERA5 and ERA5-Land reanalyses. The CHI dataset encompasses thirteen heat stress indices, including wet-bulb temperature, universal thermal climate index, mean radiant temperature, wind chill, and lethal heat stress index (Ls). Thresholds for Ls are empirically linked to mortality, enabling the identification of life-threatening heat events. Ls is sensitive to soil moisture variability, improving assessments in agricultural regions. The CHI dataset supports indoor and outdoor applications and is sensitive to humidity, radiation, and wind. Covering the global land area from 60°S to 75°N and 180°W to 180°E, it provides a unique, long-term perspective on spatial and temporal trends in heat stress, which are critical for climate impact research and adaptation planning.</p>

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A Global High-Resolution Comprehensive Heat Indices Dataset from 1950 to 2024

  • Abdul Malik,
  • Sateesh Masabathini,
  • Mohsin Ahmed Shaikh,
  • Qinqin Kong,
  • Muhammad Usman,
  • Dasari Hari Prasad,
  • Ibrahim Hoteit

摘要

Heatwaves are becoming more intense and frequent as global temperatures rise, affecting vulnerable populations, particularly in low-income communities. Addressing the impacts of heatwaves requires high-resolution data to assess their influence on labour productivity, public health, and climate risk. We introduce the Comprehensive Heat Indices (CHI) dataset, a high-resolution (0.1° × 0.1°) hourly dataset from 1950 to 2024, derived from the ERA5 and ERA5-Land reanalyses. The CHI dataset encompasses thirteen heat stress indices, including wet-bulb temperature, universal thermal climate index, mean radiant temperature, wind chill, and lethal heat stress index (Ls). Thresholds for Ls are empirically linked to mortality, enabling the identification of life-threatening heat events. Ls is sensitive to soil moisture variability, improving assessments in agricultural regions. The CHI dataset supports indoor and outdoor applications and is sensitive to humidity, radiation, and wind. Covering the global land area from 60°S to 75°N and 180°W to 180°E, it provides a unique, long-term perspective on spatial and temporal trends in heat stress, which are critical for climate impact research and adaptation planning.