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

Deep learning reveals shifting precipitation patterns on the Qinghai-Tibetan Plateau (1980-2020) linked to Southwest Asian monsoon | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep learning reveals shifting precipitation patterns on the Qinghai-Tibetan Plateau (1980-2020) linked to Southwest Asian monsoon duopingzhu, junbang wang, Hao Li, Bin Yao, Alan E. Watson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5204062/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract 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. Earth and environmental sciences/Climate sciences/Climate change/Attribution Earth and environmental sciences/Climate sciences/Hydrology meteorological observations machine learning spatial interpolation Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementalMaterial.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5204062","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381591881,"identity":"0097f630-575d-46fa-a2a5-689cce0dc1bd","order_by":0,"name":"duopingzhu","email":"","orcid":"","institution":"Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"duopingzhu","suffix":""},{"id":381591882,"identity":"6ff2a5a0-5e2e-4c0f-9407-6f1a3a107144","order_by":1,"name":"junbang wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACxhlgyoaHZC1pJGhhkACTh0nQwTy7x/Bzwa/zMvL+Z499rmCwk2dgP3sAv8PmnDGWntl3m8fwRl7yzDMMyYYNPHkJ+LXMyN0gzdsD1DKDx5ixgYE5gUGCx4CQls2/eXvO8Rj2nwFpqSdKyzZpnh8HeOQZckBaDhOhZc75b9a8Dck8BhIgLQbHDdt4cvBrMZzdlnyb54+dvTzYYRXV8vzsZwhoaQBZ1cbAYHAAxAUqZsOrHgjkweQfIKOBkNJRMApGwSgYsQAAZuU9GLXfL38AAAAASUVORK5CYII=","orcid":"","institution":"National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","correspondingAuthor":true,"prefix":"","firstName":"junbang","middleName":"","lastName":"wang","suffix":""},{"id":381591883,"identity":"cf8b8e43-5372-4956-942a-d0cf04cddf3e","order_by":2,"name":"Hao Li","email":"","orcid":"","institution":"College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, beijing,101408, China","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Li","suffix":""},{"id":381591884,"identity":"c7a31d75-7b84-4c6d-8184-d0f8cd9e80b1","order_by":3,"name":"Bin Yao","email":"","orcid":"","institution":"Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yao","suffix":""},{"id":381591885,"identity":"81ee9a9c-0789-4fee-a969-2861d348feff","order_by":4,"name":"Alan E. 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