Forests contribute to improve water quality, affect drinking water resources, and therefore influence water supply on a regional level. The forest canopy structure affects the retention of precipitation (Pr) in the canopy and hence the amount of water transferred to the forest floor termed canopy throughfall (TF). We investigated the possibilities of estimating TF based on bulk Pr and canopy structure estimated from airborne light detection and ranging (LiDAR) data. Bulk Pr and TF fluxes combined with airborne LiDAR data from 11 locations representing the most common forest types (mono-species broadleaf/coniferous and mixed forests) in Denmark were used to develop empirical models to estimate TF on a monthly, seasonal, and annual basis. This new approach offers the opportunity to greatly improve predictions of TF on catchment wide scales. Overall, results show that TF can be estimated by Pr and a canopy density metric derived from LiDAR data. In all three types of TF data sets Pr was the variable explaining the majority of the variance in TF. The proportion of explained variance adhering to the LiDAR variable increased from 1.7% for the monthly data set to 12.2% and 19.5% for seasonal and annual data sets, respectively. Although the analysis was limited to Denmark the model was successful in estimating TF for contrasting tree species (broadleaf vs. conifers) and points to a potential for extending our model approach to other similar regions. Our approach can help to assess how forest cover impacts water resources on a large scale in regions where forests play a major role in water resource management.