Multifactorial Impacts of Climatic Variables and Extreme Indices on Human Thermal Comfort in Iran

Climatic comfort, a pivotal element in elevating human well-being and health, is influenced by an intricate network of meteorological variables. This investigation employs a structural equation modeling (SEM) approach, specifically partial least squares, alongside spatial modeling, and machine learning to scrutinize the multi-faceted impact of various cli-matic constructs on composite indices of climatic comfort across Iran. The constructs under examination encompass thermal, radiative, humidity, wind, and pressure elements, as well as distinct extreme climatic phenomena. Thermal constructs such as diurnal and annual temperature ranges, dew point temperature, and annual minimum and maxi-mum temperatures are analyzed within the model, alongside radiation constructs, including shortwave radiation and albedo. Similarly, humidity and wind constructs, represented by variables like relative humidity, precipitation, and wind speed and direction, are incorporated. Furthermore, specific climatic events, such as summer days, warm nights, and the Warm Spell Duration Index (WSDI), enhance understanding of the climatic conditions shaping thermal comfort. Composite comfort indices, including the Predicted Mean Vote (PMV), Universal Thermal Climate Index (UTCI), and Wet Bulb Globe Temperature (WBGT), serve as the ultimate criteria for comfort assessment in the model. Modeling outcomes reveal that thermal conditions (with a coefficient of 0.515) and extreme climatic occurrences (with a coefficient of 0.381), notably summer days, warm nights, and heat spell duration, exert the most pronounced positive and direct influence on the composite climatic comfort indices. These findings corroborate the primary role of elevated temperatures and extreme events in engendering thermal discomfort within Iran, particularly in southern and southeastern regions (e.g., the Per-sian Gulf and Oman Sea coasts), which experience the highest intensity of these phenomena. Conversely, solar radiation demonstrated a moderate inverse effect (-0.298), while humidity conditions (-0.074) and wind/pressure (0.043) exhibited weaker impacts on comfort. The model's substantial explanatory power (R2=0.811) and robust predictive capability (Q2=0.620) underscore its high efficacy in elucidating and forecasting climatic comfort variations. Moreover, the com-plete alignment of spatial patterns derived from comfort index maps (PMV, UTCI, WBGT, Humidex, TDI) and extreme event maps (Summer Days, Warm Nights, WSDI) with the model's results validates the accuracy and credibility of the findings in pinpointing areas with significant thermal challenges (south and southwest) and regions with more favora-ble comfort levels (mountainous and northern areas). Consequently, the interplay of these constructs and their reciprocal effects plays a crucial role in shaping climatic comfort conditions across diverse Iranian locales. This research can offer substantial guidance for policymakers in devising mechanisms to enhance climatic quality and manage urban and re-gional climatic change.