In this paper, a new approach to model wave‐driven, cross‐shore shoreline change incorporating multiple timescales is introduced. As a base, we use the equilibrium shoreline prediction model ShoreFor that accounts for a single timescale only. High‐resolution shoreline data collected at three distinctly different study sites is used to train the new data‐driven model. In addition to the direct forcing approach used in most models, here two additional terms are introduced: a time-upscaling and a time‐downscaling term. The upscaling term accounts for the persistent effect of short‐term events, such as storms, on the shoreline position. The downscaling term accounts for the effect of long‐term shoreline modulations, caused by, for example, climate variability, on shorter event impacts. The multi‐timescale model shows improvement compared to the original ShoreFor model (a normalized mean square error improvement during validation of 18 to 59%) at the three contrasted sandy beaches. Moreover, it gains insight in the various timescales (storms to inter‐an-nual) and reveals their interactions that cause shoreline change. We find that extreme forcing events have a persistent shoreline impact and cause 57–73% of the shoreline variability at the three sites. Moreover, long‐term shoreline trends affect short‐term forcing event impacts and determine 20– 27% of the shoreline variability.