Abstract: The seepage and stress-deformation in an unsaturated slope under rainfall infiltration are interacted with high nonlinearity. Numerical models are commonly adopted to solve the coupled governing equations. Tremendous computational cost of numerical modeling is the main obstacle for probabilistic back analysis with field monitoring data. A probabilistic back analysis method based on polynomial chaos expansion (PCE) is proposed in this study. PCE approximation is used to construct the explicit functions between unsaturated soil parameters and model responses to replace the original numerical model. The PCE surrogate model is adopted in parameter posterior inference with Markov chain Monte Carlo (MCMC) simulation based on the Bayesian theory. An example of unsaturated soil slope under rainfall infiltration is presented to illustrate the efficiency of the proposed method. The statistics of posterior distribution and 95% uncertainty bounds obtained using the PCE-based method are close to the results of the traditional back analysis based on the original numerical model. In addition, the proposed new method can significantly improve the efficiency of model calibration.