Comparative reliability assessment of PET and UTCI thermal comfort indices using Monte Carlo simulation in urban microclimates

Urban thermal comfort analysis in hot-arid climates requires robust methodological frameworks to address intensifying heat stress. This study introduces a reliability-based thermal comfort framework using Monte Carlo simulation to evaluate six urban design interventions in Zahedan, Iran. Field measurements encompassed reference conditions, concrete gazebos with varying vegetation covers, and tree shade configurations. Monte Carlo simulation (n = 10,000) computed prediction uncertainties and probabilistic thermal comfort evaluations for Physiological Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI). A novel Thermal Comfort Reliability Index (TCRI) was developed integrating comfort probability and system reliability. The approach demonstrated excellent convergence (CV < 1%) and predictive capability (R² > 0.999, RMSE < 1.0 °C). Results revealed critical deficiencies across all interventions. The best-performing configuration (concrete gazebo with full tree shade) achieved only 20.56% comfortable conditions. Hot failure probabilities ranged from 29.85% to 65.25%. UTCI demonstrated superior reliability versus PET, with 3.14 × higher performance and improvement factors of 1.7 × to 58.2 × . Sensitivity analysis identified mean radiant temperature (r = 0.942) and air temperature (r = 0.896) as dominant variables. Extremely low TCRI values (< 5%) indicate systematic failures requiring radical climate adaptation measures in hot-arid urban areas.