Deep learning reveals shifting precipitation patterns on the Qinghai-Tibetan Plateau (1980-2020) linked to Southwest Asian monsoon

High precision precipitation estimation with high temporal and spatial resolution is essential for depicting the hydrological process in ecological and environmental researches. Various spatial interpolation algorithms were developed but large uncertainties were found for the Qinghai-Tibetan Plateau (QTP), where meteorological stations are sparsely located over its complex topography. This study developed an Attention-Gated Convolutional Neural Network (A-GCN) algorithm to produce more accurate precipitation spatial interpolation. The spatiotemporal changes were explored in the A-GCN-based precipitation in 1980 to 2020 and its underlying mechanism was analyzed in the view of Asia monsoon. The results showed the A-GCN algorithm, through local connectivity and local region weight sharing in convolutional neural networks, enable better focus on local region features, providing good performance by the comparing with independent observations or the available precipitation datasets. The spatial transition was found in the precipitation interannual trend from a decreasing north and increasing south to an increasing north and decreasing south around the year 2000. The transition could be attributed to the dipole precipitation pattern on a global scale and teleconnection with the Southwest Asia Monsoon enhancing in the early period then weakening since 2005. This study provides a state-of-the-art methodological framework for the spatial interpolation for geographic variable for regions with sparse observations. And precipitation changes would profoundly influence ecological and environment and should be paid more attentions.