## 5 Conclusions
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16081413/s1](https://www.mdpi.com/article/10.3390/rs16081413/s1)]([https://www.mdpi.com/article/10.3390/rs16081413/s1](https://www.mdpi.com/article/10.3390/rs16081413/s1)), Figure S1: Spatial distribution of RA in the wet season over the years 2015-2021; Figure S2: Spatial distribution of RF in the wet season over the years 2015-2021; Figure S3: Statistical results of RMAX3 for all grids in PLEEZ in the year with major flood; Figure S4: Spatial distribution of the flood evolution process over the years 2015-2021; Figure S5: Spatial distribution of historical simulation scenarios over the years 2015-2021; Figure S6: Collinearity analysis results between each two variables; Figure S7: Spatial change ratio of RMAX3 in different future periods (compared to 2020s); Figure S8: Spatial change ratio of population density in different future periods (compared to 2020s); Figure S9: Spatial change ratio of GDP in different future periods (compared to 2020s); Figure S10: Spatial distribution of land use in different future periods predicted by FLUS model; Figure S11: Process of generating the flood inundation-prone zone; Figure S12: Error Histogram of RMAX3 between the future projected precipitation dataset (NEX-GDDP) and the historical satellite-precipitation dataset (CHIRPS); Figure S13: Error Analysis of population density between the future projected dataset and the historical dataset in 2020; Figure S14: Error Analysis of GDP between the future projected dataset and the historical dataset in 2020; Figure S15: Overview of the case study area of the Huai River Basin (including partial Yangtze River Basin); Figure S16: A controlled experiment of the framework applied in the Huaihe River Basin; Table S1: Classification criteria of Proximity Level to river systems; Table S2: Statistics on the flood evolution process over the years 2015-2021; Table S3: Multicollinearity results of factors; Table S4: Model prediction accuracy of land use; Table S5: The validation of response performance of the framework to extreme and regular floods in Huaihe River Basin. References [88; 89; 90; 91; 92] are also cited in Supplementary Materials file. Conceptualization, K.Y., S.Y., Z.W. and S.L.G. ; methodology, K.Y., S.Y. and C.J. ; software, K.Y., W.L. and J.H. ; validation, K.Y. and Y.L. ; formal analysis, K.Y., S.Y. and Z.W. ; investigation, Z.Z. and Y.S. ; resources, S.Y., J.H. and C.J. ; data curation, K.Y. and W.L. ; writing--original draft preparation, K.Y. ; writing--review and editing, S.Y., S.L.G. and C.J. ; visualization, K.Y. and Z.Z. ; supervision, S.Y., S.L.G. and C.J. ; project administration, S.Y. ; funding acquisition, S.Y. and Y.S. All authors have read and agreed to the published version of the manuscript. This work was financially supported by LingYan Project of the Science Technology Department of Zhejiang Province, China (No. 2022C03107), and the International Center for Collaborative Research on Disaster Risk Reduction (ICCRDRR). The raw data supporting the conclusions of this article will be made available by the authors on request. The climate scenarios used for future precipitation prediction were taken from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange and distributed by the NASA Center for Climate Simulation (NCCS). We are grateful to the anonymous reviewers and the editor for their constructive comments that helped us improve the quality of this paper. The authors declare no conflicts of interest.