Accurate estimation of landslide probability or slope failure probability when there is a rainstorm event is crucial for assessment and mitigation of rainfall-induced landslide risk. Slope reliability methods provide a rigorous way of estimating landslide probability based on slope failure mechanism and probability theory. However, it is well recognized in literature that the landslide probability estimated from existing slope reliability analysis methods are often much larger than the observed landslide frequency. To improve accuracy of the slope failure probability, this study proposes a slope reliability analysis method for an existing slope at a specific site, which considers both rainfall triggering mechanism and the slope's performance records during previous rainfall events. It was found that the fact that the slope survived from previous rainfall events could be utilized to effectively reduce uncertainties in soil parameters and might reduce the estimated landslide probability by one to two orders of magnitudes. In addition, the proposed method provides a real-time slope failure probability for a given rainfall event, and the estimated slope failure probability varies as the considered rainfall event evolves with time. • A method is proposed to estimate real-time slope failure probability under a target rainfall. • Bayesian updating of uncertainties is performed using slope performance records from past rainfall events. • Uncertainties in soil and other parameters are significantly reduced by Bayesian updating. • The challenge of overestimating slope failure probability is tackled.