The Asian water tower (AWT), home to the Tibetan Plateau, is a critical source of water for 10 major river systems and supports the lives of about 2 billion people. Reliable precipitation data is essential for understanding the water cycle in this region. However, observed precipitation over the AWT is significantly underestimated, leading to paradoxes in water balance calculations. These paradoxes include actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent (SWE) exceeding contemporaneous precipitation. The underestimation is attributed to instrumental errors, such as wind-induced undercatch in precipitation gauges, and representativeness errors due to sparse and uneven gauge distribution and complex surface conditions. To address these issues, the study suggests enhancing precipitation monitoring with instruments resistant to wind and capable of measuring solid precipitation. Grid-scale bias-correction experiments using comprehensive monitoring setups can help mitigate representativeness errors. Increasing the density of ground-based precipitation gauges, especially in data-sparse regions, is also crucial. Additionally, developing a specialized assimilation system for the AWT is needed to improve reanalysis data accuracy. These measures, combined with advanced techniques like deep learning, can help resolve precipitation biases, leading to more accurate water cycle assessments and better resource management in the AWT region.The Asian water tower (AWT), home to the Tibetan Plateau, is a critical source of water for 10 major river systems and supports the lives of about 2 billion people. Reliable precipitation data is essential for understanding the water cycle in this region. However, observed precipitation over the AWT is significantly underestimated, leading to paradoxes in water balance calculations. These paradoxes include actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent (SWE) exceeding contemporaneous precipitation. The underestimation is attributed to instrumental errors, such as wind-induced undercatch in precipitation gauges, and representativeness errors due to sparse and uneven gauge distribution and complex surface conditions. To address these issues, the study suggests enhancing precipitation monitoring with instruments resistant to wind and capable of measuring solid precipitation. Grid-scale bias-correction experiments using comprehensive monitoring setups can help mitigate representativeness errors. Increasing the density of ground-based precipitation gauges, especially in data-sparse regions, is also crucial. Additionally, developing a specialized assimilation system for the AWT is needed to improve reanalysis data accuracy. These measures, combined with advanced techniques like deep learning, can help resolve precipitation biases, leading to more accurate water cycle assessments and better resource management in the AWT region.