The article "Understanding the Asian Water Tower requires a redesigned precipitation observation strategy" by Chiyuan Miao, Walter W. Immerzeel, Baiqing Xu, Kun Yang, Qingyun Duan, and Xin Li, highlights the critical role of reliable precipitation data in understanding the water cycle within the Asian Water Tower (AWT), which supports the lives of approximately 2 billion people. The authors reveal that the "observed" precipitation over the AWT is significantly underestimated based on evidence from evapotranspiration, runoff, and accumulated snow. They identify three paradoxes: actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent exceeding contemporaneous precipitation. These paradoxes are attributed to instrumental errors caused by wind-induced gauge undercatch and representativeness errors due to sparse and uneven gauge density, as well as the complexity of local surface conditions. The authors propose several solutions, including developing more robust precipitation monitoring instruments, conducting grid-scale bias-correction experiments, and increasing the density of ground-based precipitation gauges, especially in high-altitude and data-sparse regions. They also emphasize the need for an assimilation system specifically designed for the AWT to address the significant bias in current reanalysis data.The article "Understanding the Asian Water Tower requires a redesigned precipitation observation strategy" by Chiyuan Miao, Walter W. Immerzeel, Baiqing Xu, Kun Yang, Qingyun Duan, and Xin Li, highlights the critical role of reliable precipitation data in understanding the water cycle within the Asian Water Tower (AWT), which supports the lives of approximately 2 billion people. The authors reveal that the "observed" precipitation over the AWT is significantly underestimated based on evidence from evapotranspiration, runoff, and accumulated snow. They identify three paradoxes: actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent exceeding contemporaneous precipitation. These paradoxes are attributed to instrumental errors caused by wind-induced gauge undercatch and representativeness errors due to sparse and uneven gauge density, as well as the complexity of local surface conditions. The authors propose several solutions, including developing more robust precipitation monitoring instruments, conducting grid-scale bias-correction experiments, and increasing the density of ground-based precipitation gauges, especially in high-altitude and data-sparse regions. They also emphasize the need for an assimilation system specifically designed for the AWT to address the significant bias in current reanalysis data.