Study of Downscaling from CMIP6 Global Climate Model by Using Terrain-Guided Multi-Scale Residual Dense Network

CHENG Yong; GU Yakang; WANG Jun; WANG Yixuan; WANG Wei; HE Jiaxin

doi:10.16032/j.issn.1004-4965.2025.052

Abstract：CMIP6 global climate model(GCM)is one of the primary tools for predicting future climate change.However,their outputs usually have a coarse spatial resolution(typically≥1 °),making them difficult to apply directly to regional-scale climate change studies.To address this issue,this study proposes a Terrain-Guided Multi-scale Residual Dense Network(TGMSRDN)downscaling model,aiming to improve the spatial resolution and accuracy of GCM daily mean temperature data for Southwest China.Specifically,the model constructs a Multi-scale Residual Dense Block(MSRDB)to extract of multi-scale feature information from the coarse-resolution temperature data.In addition,a Terrain-Guided Network(TGN)is designed to fully leverage topographic information.This network effectively aggregates temperature data with topographic information via an attention mechanism,thereby recovering finer spatial details in the temperature data.Comparative experiments conducted in Southwest China demonstrate that TGMSRDN can effectively improve the spatial resolution of the GCM daily mean temperature from 1 ° to 0.1 °,and performs better when compared with several advanced deep-learning-based super-resolution methods.Finally,the proposed model is applied to downscale the temperature projection data for the study area during 2015-2050 under four scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results indicate that the annual mean temperature of the study area exhibits an increasing trend under all four scenarios.Notably,the temperature rise in the study area is projected to exceed 1.5 ℃ by 2050 under the SSP5-8.5 scenario.