Groundwater depletion in arid and semi-arid regions is a pressing global challenge, driven by intensive extraction for irrigation and compounded by climate variability. However, distinguishing between the impacts of anthropogenic pumping and climate variability on groundwater dynamics remains difficult due to the lagged response of groundwater levels and the scarcity of long-term abstraction records. This uncertainty limits effective groundwater management. Previous studies classify climate-influenced sites using time-series models based primarily on calibration fit rather than predictive performance. Here, we advance groundwater driver attribution by explicitly predicting groundwater heads 2 to 8 years ahead in addition to calibration fit, using commonly available climate data and observed groundwater heads. We undertake this by assessing calibration and predictive performance across 92 wells in North Gujarat, western India, using the HydroSight time-series model. By integrating probabilistic forecasting metrics, particularly the Continuous Ranked Probability Score (CRPS), with traditional calibration measures – Coefficient of Efficiency (CoE), we identified climate-dominated wells with greater spatio-temporal consistency. CRPS effectively differentiated groundwater wells influenced by climate from those affected by pumping, revealing distinct regional patterns in groundwater dynamics. We identified 37–51 % of wells as climate-dominated across multiple prediction periods, primarily in eastern districts, validated by field observations and regional assessments. This study advances groundwater assessment methods by demonstrating limitations of conventional calibration-based approaches and advocating predictive skill evaluation. Our data-driven classification uniquely relies on observed groundwater head data to assess climate influence at finer resolution, offering insights at a scale not previously explored. These findings support improved groundwater management strategies and guide sustainable use policies in data-scarce regions.