Environmental changes have led to non-stationary ood risks in coastal cities. How to quantitatively characterize the future change trend and effectively adapt is a frontier scienti c problem that needs to be solved urgently. To this end, this study uses the 2010 Shanghai land use data as the base and uses the GeoSOS-FLUS model to simulate future land use change scenarios (2030, 2050, and 2100). Based on the results of storm and ood numerical simulations, probabilistic risk, and other multidisciplinary methods, extreme storm and ood risks of various land uses (residential land, commercial and public service land, industrial land, transportation land, agricultural land, and other land) in Shanghai are analyzed and 4 adaptation strategies to deal with extreme ooding have been developed. The research results show that: 1) Under the two emission scenarios, residential, commercial and public service, and industrial land have the highest exposure assets. Under the RCP8.5 scenario, the exposure of assets in 2100, 2050, and 2030 will be 1.7 times, 1.5 times, and 1.3 times that in 2010 for 1/1000-year, respectively; the losses will be 2.7 times, 2.0 times, and 1.8 times that in 2010, respectively. 2) The spatial pattern of loss, which forms the scattered distribution of 1/10-year, is mainly distributed on both sides of the Huangpu River. For 1/1000-year, which is mainly gradually showed a strip distribution, continuous distribution of the city center, and the Qingpu-Songjiang depression in the southwest are high-risk areas for storm oods.3) The risks are mainly distributed in the city center, the lower reaches of the Huangpu River, the northern shore of Hangzhou Bay, the Qingpu-Songjiang depression in the southwest, and Chongming Island (southwest and northeast). Our work can provide decisionmaking basis for risk-sensitive based urban planning, ood risk adaptation, and resilience building in Shanghai. The methodology can also provide a reference for risk assessment in other coastal areas.