# Predicting Runoff from the Weigan River under Climate Change  
[PERSON]  
1 College of Water Conservancy & Architectural Engineering, Shihzei University, Shihzei 832000, China; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (C.R. ); [EMAIL_ADDRESS] (F.C.) 2 China Institute of Water Resources and Hydropower Research, Beijing 100038, China; [EMAIL_ADDRESS] (3) School of Civil Engineering, Tianjin University, Tianjin 300072, China; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS] (X.D. ); [EMAIL_ADDRESS] (A.L.) [PERSON]  
2 China Institute of Water Resources and Hydropower Research, Beijing 100038, China; [EMAIL_ADDRESS] (3) School of Civil Engineering, Tianjin University, Tianjin 300072, China; [EMAIL_ADDRESS] ([PERSON]  
there is a certain degree of variability among them. Therefore, it is particularly important to harmonise the spatial resolution of different GCMs and downscale their bias revision before future climate prediction. Currently, there are many statistical downscaling bias correction methods used by scholars at home and abroad. For example, [PERSON] et al. [9] used the quantile delta mapping method for statistical downscaling to assess future precipitation changes and the occurrence of hydrological droughts in South Asia, and [PERSON] et al. [10] used a bias-corrected downscaling method to process four GCMs and assessed and analysed historical trends. [PERSON] et al. [11], [PERSON] et al. [12], [PERSON] et al. [13] and other scholars processed multiple GCMs under different scenarios with bias correction, and assessed and analysed the simulation ability of these models in terms of temperature and precipitation characteristics in China. [PERSON] et al. [14] used ensemble averaging of five CMIP6 models to predict the spatial and temporal changes of blue-green water in the Wujiang River Basin, and quantitatively analysed its response to climate and land-use changes. [PERSON] et al. [15] used a weighting strategy to place constraints on 28 GCMs based on CMIP6 and carried out a simulation prediction analysis of the historical and future runoff change of the Yangtze River Basin. [PERSON] et al. [16] evaluated the simulation ability of 11 climate models based on CMIP6 at Shiyang River Basin using observational data and downscaled their climate data using the Equidistant Cumulative Distribution Function Method (EDCDFm) to obtain the future climate change trend in the basin. [PERSON] et al. [17] used the daily bias correction (DBC) method based on quantile mapping to explore the applicability of six climate models for the Hetan River Basin, and applied the \(r\)-MME (\(r\)-Multi-Model Ensemble) averaging method to calculate the bias correction results of each model in order to analyse the characteristics of the future temporal and spatial evolution of temperature and precipitation. [PERSON] et al. [18] predicted the annual and seasonal changes in temperature and precipitation in Xinjiang in the 21 st century under three different scenarios using the data from 20 models, which demonstrated good simulation ability of temperature and precipitation in Xinjiang. It can be seen that, following the introduction of the CMIP6 climate model, many scholars have used various downscaling methods to apply GCMs to their study areas, based on which of the impacts of climate change can be further explored. Climate change will inevitably cause changes in the global hydrological cycle processes and directly or indirectly affect precipitation, evaporation, runoff, soil, humidity, etc. [19]. It will not only lead to the redistribution of water resources in time and space, and increase the frequency and intensity of extreme disasters such as floods and droughts, but will also increase evapotranspiration, change rainfall and rainfall distribution, and lead to changes in land/crop suitability [20]. In this paper, from the perspective of water resources, the flow-producing area of the Weigan River is taken as an example.