A probabilistic nowcasting technique based on the Local Lagrangian method is combined with probabilistic forecasts derived from a time-lagged convection-permitting model to produce seamless short-term probabilistic precipitation forecasts. The fraction, the neighbourhood and the mean method are used to derive probabilistic information from this eight member ensemble. The model-based forecasts are calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts is evaluated with three quality measures. Probabilistic model-based forecasts are found to outperform probabilistic radar-based nowcasts after 2.25-3.5 h. Weighting functions derived in a lead time dependent evaluation of forecast skill are used to combine nowcasts and forecasts additively. The resulting seamless blended forecasts maintain or exceed the skill of the respective best component. In comparison with similar studies, the application of the time-lagged approach increases the skill of the numerical forecasts and hence the blended forecasts.