Based on the observational dataset CN05.1 and the Coupled Model Intercomparison Project (CMIP), this study assesses the performance of CMIP5 and CMIP6 projects in projecting mean precipitation at annual and seasonal timescales in the Yangtze River Basin of China over the period 2015–2020 under medium emission scenarios (RCP4.5/SSP2-4.5). Results indicate that the multi-model ensemble (MME) of CMIP6 overall has lower relative bias and root-mean square error of both annual and seasonal mean than that of CMIP5, except for winter, but both of the two ensembles show the best projected accuracy in winter. Generally, CMIP6 outperformed CMIP5 in capturing spatial and temporal pattern over the YRB, especially in the midstream and downstream areas, which have high precipitation. Further analyses suggest that the CMIP6 GCMs have lower median normalized root-mean square error than CMIP5 GCMs. Based on the Taylor skill (TS) score, both CMIP6 and CMIP5 GCMs are ranked to evaluate relative model performance. CMIP6 GCMs have higher ranks than CMIP5 GCMs, with an average TS score of 0.68 (0.55) for CMIP6 (CMIP5), and three out of the five highest scored GCMs are CMIP6 GCMs. However, the CMIP6 precipitation projections are still quite uncertain, thus requiring further assessment and correction.

Flash-flood disasters pose a serious threat to lives and property. To meet the increasing demand for refined and rapid assessment on flood loss, this study exploits geomatic technology to integrate multi-source heterogeneous data and put forward the comprehensive risk index (CRI) calculation with the fuzzy comprehensive evaluation (FCE). Based on mathematical correlations between CRIs and actual losses of flood disasters in Weifang City, the direct economic loss rate (DELR) model and the agricultural economic loss rate (AELR) model were developed. The case study shows that the CRI system can accurately reflect the risk level of a flash-flood disaster. Both models are capable of simulating disaster impacts. The results are generally consistent with actual impacts. The quantified economic losses generated from simulation are close to actual losses. The spatial resolution is up to 100 × 100 m. This study provides a loss assessment method with high temporal and spatial resolution, which can quickly assess the loss of rainstorm and flood disasters. The method proposed in this paper, coupled with a case study, provides a reliable reference to loss assessment on flash floods caused disasters and will be helpful to the existing literature.