Global change is expanding the ecological niche of mixed-severity fire regimes into ecosystems that have not usually been associated with wildfires, such as temperate forests and rainforests. In contrast to stand-replacing fires, mixed-severity fires may result in delayed tree mortality driven by secondary factors such as post-fire environmental conditions. Because these effects vary as a function of time post-fire, their study using commonly applied logistic regression models is challenging. Here, we propose overcoming this challenge through the application of time-explicit survival models such as the Kaplan-Meier (KM-) estimator and the Cox proportional-hazards (PH-) model. We use data on tree mortality after mixed-severity fires in beech forests to (1) illustrate temporal trends in the survival probabilities and the mortality hazard of beech, (2) estimate annual survival probabilities for different burn severities, and (3) consider driving factors with possible time-dependent effects. Based on our results, we argue that the combination of KM-estimator and Cox-PH models have the potential of substantially improve the analysis of delayed post-disturbance tree mortality by answering when and why tree mortality occurs. The results provide more specific information for implementing post-fire management measures.