Simple but effective models are needed for the prediction of rainfall interception under a full range of environmental and management conditions. The Liu model was validated using data published in the literature and was compared with two leading models in the literature: the Rutter and the Gash models. The Liu model was tested against the Rutter model on a single-storm basis with interception measurements observed from an old-growth Douglas fir (Pseudotsuga menziesii) forest in Oregon, USA. Simulated results by the Liu model were close to the measurements and comparable to those predicted by the Rutter model. The Liu model was further tested against the Gash model on a multistorm basis. The Gash and Liu models successfully predicted long-term interception losses from a broad range of 20 forests around the world. Results also indicated that both the Gash and the Liu models could be used to predict rainfall interception using daily rainfall data, although it was assumed in both models that there is only one storm per rain day. The sensitivity of the Liu model to stand storage capacity, canopy gap fraction and evaporation rate from wet canopy surface during rainfall was investigated. Results indicate that the Liu model has the simplest form, least data requirements and comparable accuracy for predicting rainfall interception as compared with the Rutter and the Gash models.