## 2 Materials and Methods
### Data  
The satellite rainfall product-based CHIRPS dataset was selected as a source of historical precipitation data due to its high-resolution and open access nature [41]. CHIPRS incorporates satellite imagery with in situ station data to create gridded rainfall time series. The inversion procedure includes (i) estimating precipitation by utilizing algorithms that correlate cloud temperatures with rainfall, (ii) integrating the results with ground station observations by using a novel spatial interpolation method to optimize the estimates and (iii) performing quality control and correction to ensure consistency with long-term climate trends. This dataset has been proven to perform well in southern China's river basins, especially in accurately capturing heavy rainfall that triggers hydro-morphological processes [42; 43]. Considering that floods in the PLEEZ usually occurred in the wet season from June to August, we cropped daily rainfall from these three months for the period of 2015-2021 by using Google Earth Engine (GEE) [44]. To extract historical flood introductions, we utilized the Sentinel-1 ground-range-detected products provided by the European Space Agency Copernicus program [45], which can be openly accessed and processed online on GEE. Synthetic aperture radar (SAR), compared with optical sensors, has a powerful penetration ability which allows for the accurate identification of water bodies without interference from clouds and rain [46]. When using SAR satellite imagery, the commonly used method of flood detection involves capturing the significant reduction in the SAR-derived backscatter coefficient of submerged areas compared with other non-flooded ground objects, which can be observed in a bimodal histogram by using the double-Gaussian model [47]. Sentinel-1 data have been used in flood mapping in different areas of the world [48; 49; 50; 51; 52]; for instance, their effectiveness for flood detection assessment was evaluated in Europe, and it was found to be superior to that of optical Sentinel-2 data [53]. The dual-satellite Sentinel-1 constellation has a revisit period of 6 days, enabling the continuous monitoring of prolonged flooding process, which is routinely applied in flood mapping [38; 54]. Other datasets including elevation, normalized differential vegetation index (NDVI), river and gross domestic product (GDP) data were obtained from the Resource and Environment Science and Data Center. The topographic wetness indices (TWIs) provided by [PERSON] et al. [55] are accessible on GEE. Population density per kilometer grid at the country level was estimated with WorldPop [56]. The land-use data were based on the dynamic land cover map at a 100 m resolution released by the Copernicus Global Land Service (CGLS-LC100), known for a global accuracy of over 80% [57]. For establishing future scenarios, precipitation data were based on NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), including downscaled projections for RCP 4.5 and RCP 8.5 from 21 models and scenarios, for which daily scenarioswere produced and distributed under Coupled Model Intercomparison Project Phase 5 (CMIP5). The high-resolution gridded population projection dataset for China under the shared socioeconomic pathways (SSPs) was constructed by [PERSON] et al. [58], while future GDP data were aggregated from the dataset for Chinese gridded value-added primary, secondary and tertiary industries [59; 60]. RCPs represent the development of scenario sets containing emission, concentration and land use trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) [61]. SSPs, on the other hand, build upon greenhouse gas concentration scenarios and incorporate societal changes such as those in population, education and government policies in relation to climate targets.