Stochastic modeling is widely employed to charac-terize uncertainty propagation in fluctuating wearable antenna systems. A major challenge that hinders the convergence of sto-chastic models is the multilevel random effects on antenna’s far-field caused by random disturbances, which exacerbate the already difficult inherent issue tied to high dimensionality and nonlinearity. This paper proposes to separately model the “glob-al” random effect depending mainly on frequency, and the “fine” random effect depending mainly on antenna’s directional char-acteristics. The “decoupling” of global and fine effects is obtained by separately modeling the reflection coefficient S11 and a newly defined “desensitized” far-field, which is insensitive to detuning (or mismatch) phenomena. A “centering” technique based on cross-correlation is used to reduce the sensibility of S11 to the randomness. The whole strategy significantly accelerates the convergence of the modeling process, resulting in a “bi-level” surrogate model that exhibits enhanced robustness and accuracy. Comparative tests on a flexible textile patch antenna demonstrate that the proposed technique can reduce modeling costs by 57% while maintaining the same level of model accuracy. The proposed solution could expand the application of stochastic modeling to a broader spectrum of antenna characterization and optimization.