Accurate water budget closure is critical for sustainable water resource management facing increased pressures from climate change and human activities. Although error reduction methods for individual water balance components have advanced, persistent biases remain due to the independent development of datasets, impacting basin scale water budget balance. In this research, we analyzed the mathematical origin of the bias between water budget components and developed a new basin-scale water balance calibration method that redistributes errors across components while enforcing water balance constraints. Validation confirms systematic improvements, with reduced RMSE (Precipitation:-2.29 mm/month; ET:-1.34 mm/month) and increased R2 against in situ observations. Applied to the Jinghe River Basin (2000-2019), the calibrated data reveal declining precipitation (-1.70 mm/year) and evapotranspiration (-1.84 mm/year) alongside slightly increasing runoff (0.20 mm/year in basin depth), signaling a drying trend. Land cover changes-marked by cropland loss (-3,497 km2) and forest ( + 720 km2) and grassland ( + 2,776 km2) expansion-reflect improved water consumption requirements by ecosystem, raising concerns for water retention and ecosystem stability. The method is particularly effective for ungauged basins with sparse ground data and underscores the need for integrated land-water management to enhance long-term resilience.