Current state-of-the-art thermoregulatory models do not simulate body temperature responses with the accuracies that are required for the development of automatic cooling control in liquid cooling garment (LCG) systems. Automatic cooling control would be beneficial in a variety of space, aviation, military, and industrial environments. It would optimize cooling efficiency, aid in making LCGs as portable and practical as possible, alleviate the individual from manual cooling control, and improve thermal comfort and cognitive performance. In this study, we implement an available state-of-the-art thermoregulatory model in a LCG environment and compare the thermal model response with experimental data for a 700 W rectangular type metabolic rate schedule. We modify the blood flow dynamics of the thermoregulatory model and identify a new vasoconstriction signal, i.e., the rate of change of hypothalamus temperature weighted by the hypothalamus error signal, which governs the thermoregulatory response during conditions of simultaneously increasing core and decreasing skin temperatures. With this new vasoconstriction dependency, the thermoregulatory model simulates rectal and mean skin temperature responses with root mean square deviations of and , respectively, which results in 40% and 17% reductions in the mean and peak body heat storage errors, respectively. Although the new model’s mean body heat storage error is within the allowable by an 11% margin, the peak body heat storage error exceeds the allowable by 222%, indicating that further refinements are needed. With additional improvements to the set-point temperatures, the central blood pool formulation, and the LCG boundary condition, it seems possible to achieve the strict accuracy that is needed for the development of automatic cooling control in LCG systems.