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In the context of climate change, coastal flood risk is intensifying globally, particularly in China, where intricate coastlines and frequent tropical cyclones make storm surges a major concern. Despite local government's efforts to initiate coastal monitoring networks and qualitative risk guidelines, there remains a gap in detailed and efficient quantitative assessments for combinations of multiple sea‐level components. To address this, we develop the Tropical Cyclone Storm Surge‐based Flood Risk Assessment under Combined Scenarios (TCSoS‐FRACS). This framework integrates impacts of storm surges, high tides, and sea‐level rise using a hybrid of statistical and dynamic models to balance reliability and efficiency. By combining hazard, exposure, and vulnerability, it incorporates economic and demographic factors for a deeper understanding of risk composition. Applying TCSoS‐FRACS to Hainan Island reveals that the combined effects of storm surges, high tides, and sea‐level rise significantly amplify local coastal flood risk, increasing economic losses to 4.27–5.90 times and affected populations to 4.96–6.23 times. Additionally, transitioning from Fossil‐fueled Development (SSP5‐8.5) to Sustainability (SSP1‐1.9) can reduce the risk increase by approximately half. The equivalence in flood hazard between current high tides and future sea level under a sustainable scenario boosts confidence in climate change adaptation efforts. However, coastal cities with low hazard but high exposure need heightened vigilance in flood defense, as future risk could escalate sharply. Our study provides new insights into coastal flood risk on Hainan Island and other regions with similar profiles, offering a transferable and efficient tool for disaster risk management and aiding in regional sustainable development. Plain Language Summary: Climate change is intensifying coastal flood risk worldwide. We developed TCSoS‐FRACS, a framework that assesses flood risk from storm surges, high tides, and sea‐level rise, using a combination of dynamic and statistical models. This framework evaluates hazard, exposure, and vulnerability to predict affected populations and economic losses across various land uses. When applied to Hainan Island, it shows that the combination of storm surges, high tides, and rising sea levels significantly increases flood risk. However, transitioning from a Fossil‐fueled Development Path to a Sustainability Path can reduce this increased risk almost by half. The study also finds that, on Hainan Island, current high tide hazards may be comparable to future sea‐level rise hazards under a sustainable scenario, boosting confidence in adaptation efforts. Coastal cities with low hazard but high exposure need greater vigilance, as their future flood risk could rise sharply. Our research offers valuable insights and tools for managing coastal flood risk in Hainan Island and similar regions, supporting sustainable development and effective disaster management. Key Points: Design a flood risk framework for TC storm surges combined with high tides and sea‐level rise using hybrid dynamic and statistical modelsEquivalence in flood between current high tides and future sea level under a sustainable scenario boosts confidence in adaptation effortsCoastal cities with low hazard but high exposure need heightened vigilance in flood defense as future risk could dramatically escalate [ABSTRACT FROM AUTHOR]

Strategies of community‐based disaster risk reduction have been advocated for more than 2 decades. However, we still lack in‐depth quantitative assessments of the effectiveness of such strategies. Our research is based on a national experiment in this domain: the "Comprehensive Disaster Reduction Demonstration Community" project, a governmental program running in China since 2007. Information on more than 11,000 demonstration communities was collected. Combined with the local disaster information and socioeconomic conditions, the spatiotemporal characteristics of these communities over 12 years and their differences in performance by region and income group were analyzed. We performed an attribution analysis for disaster risk reduction effectiveness. This is the first time a series of quantitative evaluation methods have been applied to verify the effectiveness of a large‐scale community‐based disaster risk reduction project, both from the perspective of demonstrative effects and loss reduction benefits. Here, we find that the project is obviously effective from these two perspectives, and the disaster loss reduction effectiveness illustrates clear regional differences, where the regional economic level and hazard severity act as important drivers. Significant differences of urban‐rural and income call for matching fortification measures, and the dynamic management of demonstration community size is required, since the loss reduction benefit converges when the penetration rate of the demonstration community reaches approximately 4% in a province. These and further results provide diverse implications for community‐based disaster risk reduction policies and practices. [ABSTRACT FROM AUTHOR]

Coupled Model Intercomparison Project Phase 5 (CMIP5) models have limited capacity for depicting the variability of precipitation at interannual and interdecadal timescales. This study analyses the relative magnitude of interannual to multidecadal variability in annual precipitation across the world in the recently released CMIP6 dataset by quantifying the discrepancy between observed and modelled CMIP5 and CMIP6 data, considering both the absolute and relative magnitude of precipitation variability. We found that: (a) similar to CMIP5, CMIP6 models were short of capacity to simulate the non‐homogeneity in the relative magnitude of interdecadal variability, which is linked to prolonged drought and pluvials in terms of the strength value. The relative value of the interdecadal variability ranged from 15% to more than 30% in observation. Compared with observational data, the relative magnitude, having spatial uniformity, mostly ranged from 10 to 20% in CMIP5 and CMIP6. This result suggests that future projections lack a sufficient decadal variability in CMIP6, indicating a limited capacity for the prediction of floods and droughts in regions like central Africa, North America, and Amazonia; (b) Most individual models in CMIP6 had a better performance in terms of the spatial distribution of the interdecadal precipitation as compared to CMIP5. However, the absolute variations of the overall, interannual, and interdecadal precipitation of multi‐model ensemble simulations (MME) in CMIP6 were larger than those in CMIP5; (c) the underestimation of the interdecadal component in different areas was markedly decreased in East Asia, South Asia but increased in North and South Africa in CMIP6, when compared with CMIP5 and (d) For future scenarios, the higher the range of future forcing pathway is, the larger interdecadal and interannual variabilities are in all regions in CMIP6. For future persistent droughts and floods predicting in a specific region, it is necessary to consider the discrepancy between historical simulations and observational data, and select the performance‐plus models. [ABSTRACT FROM AUTHOR]

The negative impact of climate change continues to escalate flood risk. Floods directly and indirectly damage highway systems and disturb the socioeconomic order. In this study, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. The approach has three parts: (1) a multi‐agent simulation model to represent traffic, heterogeneous user demand, and route choice in a highway network; (2) a flood simulator using future runoff scenarios generated from five global climate models, three representative concentration pathways (RCPs), and the CaMa‐Flood model; and (3) an impact analyzer, which superimposes the simulated floods on the highway traffic simulation system, and quantifies the flood impact on a highway system based on car following model. This approach is illustrated with a case study of the Chinese highway network. The results show that (i) for different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years; (ii) floods in different years have variable impacts on regional connectivity; and (iii) extreme flood impacts can cause huge damages in highway networks; that is, in 2030, the estimated 84.5% of routes between provinces cannot be completed when the highway system is disturbed by a future major flood. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. The approach can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk. [ABSTRACT FROM AUTHOR]

Coastal areas typically have high social and economic development and are likely to suffer huge losses due to tropical cyclones. These cyclones have a great impact on the transportation network, but there have been a limited number of studies about tropical-cyclone-induced transportation network functional damages, especially in Asia. This study develops an innovative measurement and analytical tool for highway network functional damage and risk in the context of a tropical cyclone, with which we explored the critical spatial characteristics of tropical cyclones with regard to functional damage to a highway network by developing linear regression models to quantify their relationship. Furthermore, we assessed the network's functional risk and calculated the return periods under different damage levels. In our analyses, we consider the real-world highway network of Hainan province, China. Our results illustrate that the most important spatial characteristics were location (in particular, the midlands), travel distance, landfalling status, and origin coordinates. However, the trajectory direction did not obviously affect the results. Our analyses indicate that the highway network of Hainan province may suffer from a 90% functional damage scenario every 4.28 years. These results have critical policy implications for the transport sector in reference to emergency planning and disaster reduction. [ABSTRACT FROM AUTHOR]

After the Wenchuan earthquake (magnitude 7.9, May 12, 2008), intensive debates on how China should establish a natural disaster insurance system were initiated among researchers, policymakers, and insurance professionals. Our focus was the social aspects of disaster insurance, explored in China through a nationwide survey. Our questionnaires investigated people's risk awareness, insurance acceptance, their opinions on governmental measures for disaster management, and their willingness to pay for disaster house insurance. We analyzed the results at both regional and individual scales. We found that the integrated hazard index and respondents' experience of insurance (considered objective factors), and their opinions on the importance of insurance and government responsibility (considered subjective factors) showed strong correlation with the regional overall acceptance of disaster insurance. An individual's decision to participate highly depended on his/her experience of both insurance and disaster and his/her opinion on the importance of insurance as a coping mechanism. Respondents from poverty-stricken or less-developed counties were not necessarily more reluctant to accept natural disaster insurance, though they exhibited relatively lower ability to afford insurance. In general, respondents had correct perceptions of natural disasters in their areas; however, people from regions with a greater multihazard threat showed less willingness to accept disaster insurance because they tended to expect the government to undertake to cover losses and considered insurance to be less important. People's willingness to pay for an assumed disaster house insurance was also investigated and analyzed. We consequently discuss the policy implications for developing a disaster insurance system in China. [ABSTRACT FROM AUTHOR]