This study aimed to improve how the Universal Thermal Climate Index, a key measure of human thermal comfort, is calculated. Existing methods use a simplified polynomial approximation that is straightforward to apply but can introduce errors. We developed a new version using sparse regression with orthogonal polynomials, which keeps computational efficiency while improving accuracy and stability. The results enable more reliable assessments of outdoor thermal comfort and climate analyses.