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The water resources of the Third Pole (TP), highly sensitive to climate change and glacier melting, significantly impact the water and food security of millions in Asia. However, projecting future spatial‐temporal runoff changes for TP's mountainous basins remains a formidable challenge. Here, we've leveraged the long short‐term memory model (LSTM) to craft a grid‐scale artificial intelligence (AI) model named LSTM‐grid. This model has enabled the production of hydrological projections for the seven major river basins of TP. The LSTM‐grid model integrates monthly precipitation, air temperature, and total glacier mass changes (total_GMC) data at a 0.25‐degree model grid. Training the LSTM‐grid model employed gridded historical monthly runoff and evapotranspiration data sets generated by an observation‐constrained cryosphere‐hydrology model at the headwaters of seven TP river basins during 2000–2017. Our results demonstrate the LSTM grid's effectiveness and usefulness, exhibiting a Nash‐Sutcliffe Efficiency coefficient exceeding 0.92 during the verification periods (2013–2017). Moreover, river basins in the monsoon region exhibited a higher rate of runoff increase compared to those in the westerlies region. Intra‐annual projections indicated notable increases in spring runoff, especially in basins where glacier meltwater significantly contributes to runoff. Additionally, the LSTM‐grid model aptly captures the runoff changes before and after the turning points of glacier melting, highlighting the growing influence of precipitation on runoff after reaching the maximum total_GMC. Therefore, the LSTM‐grid model offers a fresh perspective for understanding the spatiotemporal distribution of water resources in high‐mountain glacial regions by tapping into AI's potential to drive scientific discovery and provide reliable data. Plain Language Summary: Water resources of the Third Pole (TP) significantly impact the water and food security in Asia. However, projecting future spatial‐temporal runoff changes for the TP's mountain basins remains a challenge. Here, we've leveraged the long short‐term memory (LSTM) model to craft a gridded artificial intelligence model (named LSTM‐grid). Trained by the outputs of an observation‐constrained distributed cryosphere‐hydrology model, the LSTM‐grid has enabled reliable spatiotemporal runoff and evapotranspiration projections for the headwaters of seven TP rivers (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, Indus) till 2100. Our projections show that the river basins in the monsoon region exhibit a higher rate of runoff increase compared to those in the westerlies region. In particular, the proposed approach in this study can reasonably capture the runoff changes before and after the turning points of glacier melting without prior knowledge, highlighting the growing influence of precipitation on runoff after reaching the maximum total glacier mass changes (of a river basin). Hence, the LSTM‐grid model provides a fresh perspective for understanding the spatiotemporal distribution of water resources in high‐mountain glacial regions. Key Points: We use artificial intelligence and an observation‐constrained cryosphere‐hydrology model to project future runoff for seven high‐mountain Third Pole basinsResults show that river basins in the monsoon region exhibited a higher rate of runoff increase compared to those in the westerlies regionThe proposed approach can aptly simulate runoff changes before and after the turning points of glacier melting without prior knowledge [ABSTRACT FROM AUTHOR]

The high-mountain Third Pole (TP) in Asia is undergoing rapid warming, profoundly impacting river discharge. Changes in precipitation and the degradation of glaciers and permafrost exert a substantial impact on TP rivers, affecting millions downstream. Nevertheless, conventional estimation methods that rely on in-situ observations and models face considerable challenges due to inconsistent data quality and availability, complex terrain, and difficulty in representing hydrological processes. Here, we harnessed the power of high-resolution (3 m) Dove/PlanetScope satellite images to meticulously measure river width and propose a novel formula for estimating discharge. This approach facilitates a coherent assessment of discharge at various river section shapes. Its efficacy is demonstrated through applying to thirteen high-mountain TP rivers, revealing an estimated total mountain discharge of 3786 billion m3 during 2017–2021, equaling water from approximately 167 Great Lakes. These findings have immediate implications for integrated water resources management at the poorly-gauged transboundary basins of Asia. • KNN extracts water surface nicely from Dove images if training data is sufficient. • Q = a W 8 3 (Q , discharge; a , scaling factor; W , river width) estimates river discharge well. • Estimated total discharge from Third Pole mountains is 3786 billion m3 for 2017–2021. [ABSTRACT FROM AUTHOR]