Groundwater recharge is difficult to estimate, especially in fractured aquifers, because of the spatial variability of the soil properties and because of the lack of data at basin scale. A relevant method, known as the water table fluctuation (WTF) method, consists in inferring recharge directly from the WTFs observed in boreholes. However, the WTF method neglects the impact of lateral groundwater redistribution in the aquifer; i.e., it assumes that all the WTFs are attributable to recharge. In this study, we developed the WTF approach in the frequency domain to better consider groundwater lateral flow, which quickly redistributes the impulse of recharge and mitigates the link between WTFs and recharge. First, we calibrated a 1D analytical groundwater model to estimate hydrodynamic parameters at each borehole. These parameters were defined from the WTFs recorded for several years, independently of prescribed potential recharge. Second, calibrated models are reversed analytically in the frequency domain to estimate recharge fluctuations (RFs) at weekly to monthly scales from the observed WTFs. Models were tested on two twin sites with a similar climate, fractured aquifer and land use but different hydrogeologic settings: one has been operated as a pumping site for the last 25 years (Ploemeur, France), while the second has not been perturbed by pumping (Guidel). Results confirm the important role of rainfall temporal distribution in generating recharge. While all rainfall contributes to recharge, the ratio of recharge to rainfall minus potential evapotranspiration is frequency-dependent, varying between 20 %–30 % at periods <10 d and 30 %–50 % at monthly scale and reaching 75 % at seasonal timescales. We further show that the unsaturated zone thickness controls the intensity and timing of RFs. Overall, this approach contributes to a better assessment of recharge and helps to improve the representation of groundwater systems within hydrological models. In spite of the heterogeneous nature of aquifers, parameters controlling WTFs can be inferred from WTF time series, providing confidence that the method can be deployed in different geological contexts where long-term water table records are available.