Accurate prediction of skin temperature and transient sweating responses is essential for assessing thermal comfort in tropical climates. This study developed and validated a multi-node thermoregulation model (TROP) tailored to tropical populations by incorporating population-specific physiological parameters and segmental sweat distribution. The model was validated against experimental data from 14 participants exposed to three ambient conditions: 20°C (below thermoneutral), 24°C (thermoneutral), and 28°C (above thermoneutral), while wearing typical tropical clothing (0.04–0.22 clo). TROP predictions were compared with experimental measurements and with outputs from the established JOS-3 baseline (JOS-B) and its sweat-modified extension (JOS-SW). Validation results show that the TROP model provided the closest agreement with experimental data across both steady-state and transient phases. It reduced the root mean square error (RMSE) of mean skin temperature by 12.0% compared with JOS-SW and by 43.6% compared with JOS-B, resulting in an overall RMSE of 0.22°C. The baseline JOS-B model consistently overestimated post-transition skin temperatures, particularly during warm exposure (RMSE = 0.39°C), whereas JOS-SW slightly underestimated the final steady-state plateau (RMSE = 0.25°C), suggesting that its adjustments were only partially effective. For local skin temperatures, the TROP model kept prediction errors within 1.0°C across all eight body regions, whereas the JOS-B model showed errors exceeding 1.0°C at multiple sites. These findings demonstrate that incorporating population-specific physiological adaptations improves predictive accuracy. The proposed framework provides a robust basis for improving predictions of thermal comfort and heat stress in warm-humid climates and supports the optimization of indoor environmental controls and energy-efficient HVAC strategies.
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