Estimating groundwater level evolution is a major issue in the context of climate change. Groundwater is a key resource and can even account in some countries for more than half of the water supply. Groundwater trend estimates are often used for describing this evolution. However, the estimated trend obviously strongly depends on available time series length, which may be caused by the existence of long-term variability of groundwater resources. In this paper, using a groundwater level database in Metropolitan France as an example, we address this issue by exploring how much trend estimates are sensitive to low-frequency variability of groundwater levels. Database consists of relatively undisturbed groundwater level time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping). Frequent changes in trend direction and magnitude are detected according to time series length, which can eventually lead to contradictory interpretations of the groundwater resource evolution, as presented in first part of this article. To assess whether low-frequency variabilityknown to originate from climate variabilitycan induce such modifications of trends, we explored in a second step the multi-time scale variability of groundwater levels using a methodology based on discrete wavelet transform. Most of the time series displaying changing trends depending on time series length corresponded to aquifers with high-amplitude low-frequency variability of groundwater levels. Two predominant low-frequency components were detected: multi-annual (~7 years) and decadal (~17 years). We finally examined how much those two low-frequency components may affect trend estimates on the longer time period available. For this purpose, we individually removed each of both components from the original times series by discrete wavelet filtering and re-estimated trends in the filtered groundwater level time series. The results showed that the groundwater level trends were highly sensitive to the presence of any of these lowfrequency components, which may then strongly influence the estimated trends either by exaggerating or mitigating them. These results emphasize that i) attributing the estimated trends only to climate change would be hazardous given the large influence of low-frequency variability on groundwater level trends, ii) estimation of trends in hydrological projections resulting from General Circulation Models (GCM) outputs in which lowfrequency variability is not well represented would be subject to strong uncertainty, iii) a potential change in the amplitude of internal climate variabilitye.g. increasing or decreasing low-frequency variabilityin the next decades may lead to substantial changes in groundwater level trends.