<p>Climate variations and human activities drive significant spatial and temporal variability in groundwater dynamics in agricultural areas, posing challenges for sustainable resource management. Despite advances in monitoring technologies, existing methods like spatial interpolation and physically based numerical models are limited in capturing the complex interplay of climatic and anthropogenic factors in data-scarce or heterogeneous agricultural settings. This study develops a similarity-based classification of groundwater hydrographs using discrete wavelet transformation with hierarchical clustering combined with random forest and cross-correlation analyses. The method is applied to investigate water table (WT) dynamics across 79 monitoring wells in the unconfined aquifer of the North China Plain. The results reveal six distinct clusters representing unique patterns of WT dynamics. These clusters exhibit unique temporal periodicities and trends, spatially corresponding to specific groundwater depth ranges closely associated with crop patterns. WTs show rising trends in areas with low-irrigation cotton cultivation and declining trends in regions with high-water-demand crops. Furthermore, WT variability patterns are explained by precipitation (<i>P</i>), evapotranspiration (ET), net balance (<i>P</i>-ET), and its cumulative net balance. Among these, the cumulative net balance, representing the long-term difference between <i>P</i> and ET, was identified as the primary driver, reflecting the combined effects of groundwater abstraction for irrigation and reductions in recharge. In particular, within groundwater depression cones, WT response to precipitation-driven recharge is severely weakened. Consequently, increases in ET driven by groundwater irrigation become the dominant factor controlling continuous WT decline. Therefore, adaptation strategies such as reducing irrigation or enhancing recharge should be implemented in such areas.</p>