Groundwater level (GWL) fluctuations can be expressed over a wide range of timescales. In particular, as aquifers act as low-pass filters, low-frequency variability (from interannual to decadal timescales) originating from large-scale climate variability usually represents a significant part of GWL variance. This is typically the case of aquifers in the Seine River basin for which extreme GWL then appear largely dependent on such low-frequency fluctuations. In addition to expected trend behaviours (e.g., progressive increase in air temperature or evapotranspiration, increase or decrease of monthly or seasonal precipitation amounts, etc.), global climate change can also affect, and have maybe already affected, the internal variability as described by climate modes like for instance the El Nino Southern Oscillation (ENSO) or the North-Atlantic Oscillation (NAO), which explain more or less directly the low-frequency variability of hydrological processes including GWL. How GWL in a large hydrosystem may respond to such changes then remains an open question, and even appears as an important issue regarding extreme GWL. Would that lead to more or to less severe extremes, with more groundwater droughts or, on the contrary, more groundwater floodings? Would this be expressed in the same way for aquifers exhibiting different GWL behaviours in terms of low-frequency variance? To tackle this issue, we implemented an empirical numerical approach that would allow to assess the sensitivity of aquifers to changes in large-scale climate variability, using the whole Seine hydrosystem as a test case. The approach consisted in i) identifying and modifying the spectral content, originating from large-scale climate variability, of the precipitation field using signal processing techniques, ii) re-injecting this modified field as input to a physically-based hydrological/hydrogeological modeling tool (the CaWaQS software) for the Seine basin. We used the Safran precipitation field over the period 1970-2018, which was initially used for model calibration and validation. GWL dataset for the Seine basin is a subset of a database of climate-sensitive (i.e. with as low anthropogenic influence as possible) GWL time series recently set up at BRGM and University of Rouen Normandy. First, the Safran reanalysis and observed GWL time series were analyzed using continuous wavelet transform (CWT) to identify the relevant spectral content reflecting climate variability: interannual (2-4yr), multiannual (5-8yr) and decadal (~15yr) variabilities. Then, Safran precipitation time series were decomposed into time scale components, or so-called wavelet details, using maximum overlap discrete wavelet transform (MODWT). For each time series of the precipitation field, the amplitude of components corresponding to previously identified interannual, multiannual and decadal variabilities was modified individually, by either increasing or decreasing it by 50%; and then all components, including the one that was amplitude-modulated, were re-agregating. This eventually led to six scenarios of changing variability of precipitation. The corresponding modified precipitation fields were re-injected into CaWaQS model, which produced modified GWL time series. First results indicated that amplitude-modifications of low-frequency spectral content in precipitation field cause variance-modifications of these same time scales in GWL, which should affect extreme levels, and this will be investigated in the next few months.