Context <p>The inequality in urban thermal comfort exposure has become a pressing issue in landscape ecology, exacerbated by climate change and rapid urbanization. However, the modifiable areal unit problem (MAUP) introduces significant uncertainty in spatial assessments, undermining efforts to understand and address thermal comfort exposure inequality through landscape planning.</p> Objectives <p>This study aims to (1) evaluate how MAUP affects the measurement of thermal comfort exposure inequality and its influencing factors and (2) identify the optimal spatial scale for accurate analysis. The case study focuses on Zhengzhou’s central urban area.</p> Methods <p>Thermal comfort exposure inequality was quantified across 12 grid scales using the modified temperature‒humidity index, a population-weighted exposure model, and the Gini coefficient. We applied the optimal geodetector, gradient boosting regression tree, and multiscale geographically weighted regression (MGWR) to assess MAUP effects and spatial heterogeneity.</p> Results <p>MAUP significantly alters the explanatory power and ranking of influencing factors. A 3 × 3&#xa0;km grid was identified as the optimal spatial unit. Population density (q = 0.837) and the green space ratio (q = 0.641) were the dominant drivers. Interaction detection revealed nonlinear enhancement between key factors. MGWR confirmed the spatially heterogeneous effects on the thermal comfort exposure inequality distribution.</p> Conclusions <p>This study highlights the importance of considering MAUP in thermal comfort exposure assessments to ensure accurate and equitable urban planning. Prioritizing green space expansion and optimizing its distribution in densely populated areas can effectively reduce thermal inequality. These findings offer insights for promoting environmental justice and sustainable urban development in high-density cities, such as Zhengzhou.</p>