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Manuela De Gregori,1–3 Inna Belfer,2,4 Roberto De Giorgio,5 Maurizio Marchesini,2,3,6 Carolina Muscoli,7 Mariangela Rondanelli,2,8 Daniela Martini,9 Pedro Mena,9 Laura Isabel Arranz,2,10 Silvia Lorente-Cebrián,2,11 Simone Perna,8 Anna Villarini,12 Maurizio Salamone,2,13,14 Massimo Allegri,2,15 Michael E Schatman2,16,17 1 Pain Therapy Service, Fondazione IRCCS Polclinico San Matteo, Pavia, Italy; 2Study in Multidisciplinary Pain Research Group, Parma, Italy; 3Young Against Pain Group, Parma, Italy; 4Faculty of Dentistry, McGill University, Montreal, QC, Canada; 5Department of Clinical Sciences, Nuovo Arcispedale S. Anna, University of Ferrara, Ferrara, Italy; 6Anesthesia, Intensive Care and Pain Therapy Service, Azienda Ospedaliero, Universitaria of Parma, Parma, Italy; 7Department of Health Sciences, Institute of Research for Food Safety and Health, University “Magna Graecia” of Catanzaro, Parma, Italy; 8Department of Public Health, Section of Human Nutrition and Dietetics, Azienda di Servizi alla Persona di Pavia, University of Pavia, Pavia, Italy; 9Human Nutrition Unit, Department of Food & Drugs, University of Parma, Parma, Italy; 10Department of Nutrition, Food Sciences and Gastronomy, University of Barcelona, Barcelona, Spain; 11Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy, Center for Nutrition Research, University of Navarra, Pamplona, Spain; 12Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; 13Science department, Metagenics Italia srl, Milano, Italy; 14Società internazionale di Neuropsicocardiologia, Trapani, Italy; 15Anesthesia and Intensive Care Service – IRCCS MultiMedica Hospital, Sesto San Giovanni, Milano, Italy; 16Research and Network Development, Boston Pain Care, Waltham, MA, USA; 17Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA Abstract: This review is aimed to summarize the latest data regarding pain and nutrition, which have emerged during the second edition of Feed Your Destiny (FYD). Theme presentations and interactive discussions were held at a workshop on March 30, 2017, in Florence, Italy, during the 9th Annual Meeting of Study in Multidisciplinary Pain Research, where an international faculty, including recognized experts in nutrition and pain, reported the scientific evidence on this topic from various perspectives. Presentations were divided into two sections. In the initial sessions, we analyzed the outcome variables and methods of measurement for health claims pertaining to pain proposed under Regulation EC No 1924/2006 of the European Parliament and of the Council of 20 December 2006 on nutrition and health claims made on foods. Moreover, we evaluated how the Mediterranean diet can have a potential impact on pain, gastrointestinal disorders, obesity, cancer, and aging. Second, we discussed the evidence regarding vitamin D as a nutraceutical that may contribute to pain control, evaluating the interindividual variability of pain nature and nurture, and the role of micro-RNAs (miRNAs), polyunsaturated omega 3 fatty acids, and phenolic compounds, with a final revision of the clinical role of nutrition in tailoring pain therapy. The key take-home message provided by the FYD workshop was that a balanced, personalized nutritional regimen might play a role as a synergic strategy that can improve management of chronic pain through a precision medicine approach. Keywords: chronic pain, multidisciplinary pain management, personalized nutrition, nutritional supplements

Abstract While there is growing attention toward the changes in flood magnitude and frequency, little is known about the way climate change could impact flood duration. Here we focus on 378 streamgages across the eastern United States to develop statistical models that allow the description of the year-to-year changes in flood duration above two National Weather Service (NWS) flood severity levels (i.e., minor and moderate). We use climate-related variables (i.e., basin- and season-averaged precipitation and temperature) as predictors, and show that they can be used to describe the inter-annual variability in seasonal flood durations for both NWS flood severity levels. We then use the insights from the understanding of the historical changes to provide an assessment of the projected changes in flood durations using global climate models from the Coupled Model Intercomparison Project Phase 6 and multiple shared socio-economic pathways. Our results show that the eastern United States is projected to experience longer flood durations, especially in winter (i.e., the main flood season) and under higher emission scenarios.

Abstract The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.

Acting as ‘antioxidants’, phenolic compounds have been shown to have a potential protective effect against a wide range of noncommunicable diseases. However, their content in plant-based products – and, in turn, their potential benefits – might be affected by the thermal procedures used in food cooking. In light of this, the aim of this work was a characterization of Patata Rossa di Colfiorito (PRC, an Italian PGI red-skinned potato) and an investigation of the impact of cooking and storage on its bioactive compounds, phenolic content, and antioxidant activity. After the harvesting (T0), samples were analyzed using instrumental analysis (e.g., HPLC-ESI-MS/MS) and a set of chemical assays (total phenolic content, 2,2-Diphenyl-1-picrylhydrazyl – DPPH, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) – ABTS, and oxygen radical absorbance capacity – ORAC assays). Analyses were also performed on boiled, pressure-cooked and one-month-stored (T1) products. A commercial red-skinned potato (RSP) was also analyzed.Overall, compared with the raw product, both boiling and pressure-cooking led to a significant increase in bioactive compounds (raw: 168.15 mg kg−1 of fresh matter, FM; boiled: 398.92 mg kg−1 FM; pressure-cooked: 309.24 mg kg−1 FM), total phenolic content and antioxidant activity measured by DPPH and ABTS assays.PRC samples showed higher content in bioactive compounds, TPC values and antioxidant activity compared with commercial RSP (with some sporadic exceptions in ORAC values). Interestingly, one month of domestic storage did not affect the freshly harvested product’s features.In conclusion, these results highlighted the quality of this local product and its property to withstand thermal processing and storage. We believe that the results we have obtained should be taken into consideration by health professionals, at both local and national levels, and that PRC deserves to be further studied to investigate its potential human health benefits, as well.

Abstract Urban areas have been shown to impact rainfall by altering both its intensity and spatial structure at sub‐hourly and sub‐kilometer scales. However, there is currently no clear understanding of the precise pattern of change and the mechanisms that drive these changes. Since the hydrological response in urban areas is highly sensitive to such rainfall properties, understanding these changes is critical to improving our ability to assess urban flood risk. We use 7 years of high‐resolution weather radar data (4‐ or 5‐min and 1 km) to analyze changes in patterns of rainfall intensity, spatial structure, and storm evolution across eight urban areas within Europe and the United States. The use of the same methodology across the different cities enables a consistent comparison among them. We track convective rainfall events using a storm tracking algorithm and assess changes in rainfall properties in the upwind, center, and downwind regions of each city. We also investigate changes in the frequency of storm initiations, terminations, splitting, and merging. Our results show that urban areas act to intensify rainfall—mostly over them, but sometimes on their peripheries. Overall, larger cities tend to show the largest rainfall enhancements. Our findings highlight that rainfall spatial structure is altered over the urban core; usually resulting in more spatially concentrated rainfall. We also observe increased storm initiations over most cities and increased storm splitting over one. Given that demographic projections show that future urban population will increase, our results point toward an increased future flood risk in growing cities.

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice.

Abstract Understanding changes in the hazard component of climate risk is important to inform societal resilience planning in a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event‐based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2‐LENS). We find fewer TCs in the future, but these future storms have higher wind speeds and are wetter. We also find that TCs produce heavier 3‐day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200‐year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or the statistical downscaling method. It can also produce quite different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event‐based simulations in the context of downscaling method limitations.

Abstract Understanding projected changes in flooding across the contiguous United States (CONUS) helps increase our capability to adapt to and mitigate against this hazard. Here, we assess future changes in flooding across CONUS using outputs from 28 global climate models and four scenarios of the Coupled Model Intercomparison Project Phase 6. We find that CONUS is projected to experience an overall increase in flooding, especially under higher emission scenarios; there are subregional differences, with the Northeast and Southeast (Great Plains of the North and Southwest) showing higher tendency towards increasing (decreasing) flooding due to changes in flood processes at the seasonal scale. Moreover, even though trends may not be detected in the historical period, these projected future trends highlight the current needs for incorporating climate change in the future infrastructure designs and management of the water resources.

Abstract Extreme flood events have regional differences in their generating mechanisms due to the complex interaction of different climate and catchment processes. This study aims to examine the capability of climate drivers to capture year‐to‐year variability in global flood extremes. Here, we use a statistical attribution approach to model seasonal and annual maximum daily discharge for 7,886 stations worldwide, using season‐ and basin‐averaged precipitation and temperature as predictors. The results show robust performance of our seasonal climate‐informed models in describing the inter‐annual variability in seasonal and annual maximum discharges regardless of the geographical region, climate type, basin size, degree of regulation, and impervious area. The developed models enable the assessment of the sensitivity of flood discharge to precipitation and temperature changes, indicating their potential to reliably project changes in the magnitude of flood extremes.

Abstract This study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of the estimates of future annual exceedance probabilities (AEPs) are expected to increase under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that play a more limited role compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes' projections and useful information for climate planning across the state.

Abstract The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive.

Abstract Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth’s surface. The strength of a landscape’s connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics, with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world’s most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that, as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity with implications for human and environmental health.

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

The purpose of this study is to assess the cardiometabolic responses of a lifestyle intervention (LI) conducted at home among breast cancer (BC) survivors during the two years of COVID-19 pandemic. A 3-month LI focused on diet and exercise was performed on thirty BC survivors (women; stages 0-II; non-metastatic; aged 53.6 ± 7.6 years; non-physically active) with a risk factor related to metabolic/endocrine diseases. Anthropometrics, cardiorespiratory fitness (V˙ O2max), physical activity level (PAL), adherence to the Mediterranean diet (MeDiet modified questionnaire), and several biomarkers (i.e., glycemia, insulin, insulin resistance [HOMA-IR] index, triglycerides, high- [HDL] and low- [LDL] density lipoproteins, total cholesterol, progesterone, testosterone, and hs-troponin) were evaluated before and 3-, 6-, 12-, and 24-month after the LI. Beneficial effects of the LI were observed on several variables (i.e., body mass index, waist circumference, MeDiet, PAL, V˙ O2max, glycemia, insulin, HOMA-IR index, LDL, total cholesterol, triglycerides, testosterone) after 3-month. The significant effect on Mediterranean diet adherence and V˙ O2max persisted up to the 24-month follow-up. Decreases in HOMA-IR index and triglycerides were observed up to 12-month, however did not persist afterward. This study provides evidence on the positive association between LI and cardiometabolic health in BC survivors.

Abstract We explore the projected changes in flood impacts across Iowa (central United States) and the associated uncertainties by forcing a hydrologic model with downscaled global climate model outputs and four Shared Socioeconomic Pathways. Our results point to projected increasing magnitude and variability in flooding across the state, especially for high‐emission scenarios. Next, we partition the flood impacts' projections into: (a) the response of the global climate models to anthropogenic forcing, (b) scenario uncertainty due to emissions, and (c) internal climate variability. We find scenario uncertainty plays a small role, while climate model uncertainty and internal climate variability dominate the flood impacts' projections, with the contribution of model uncertainty increasing toward the end of this century. Insights from our work can be utilized by stakeholders to understand the current limitations of flood impact projections and provide suggestions about where modelers should focus efforts to reduce uncertainty.

Abstract Background Breast cancer (BC) is the most common invasive cancer in women, and exercise can significantly improve the outcomes of BC survivors. MoviS (Movement and Health Beyond Care) is a randomized controlled trial aimed to evaluate the potential health benefits of exercise and proper nutritional habits. This study aims to assess the efficacy of aerobic exercise training in improving quality of life (QoL) and health-related factors in high-risk BC. Methods One hundred seventy-two BC survivor women, aged 30–70 years, non-metastatic, stage 0–III, non-physically active, 6–12 months post-surgery, and post chemo- or radiotherapy, will be recruited in this study. Women will be randomly allocated to the intervention arm (lifestyle recommendations and MoviS Training) or control arm (lifestyle recommendations). The MoviS training consists of 12 weeks of aerobic exercise training (2 days/week of supervised and 1 day/week of unsupervised exercise) with a progressive increase in exercise intensity (40–70% of heart rate reserve) and duration (20–60 min). Both arms will receive counseling on healthy lifestyle habits (nutrition and exercise) based on the World Cancer Research Fund International (WCRF) 2018 guidelines. The primary outcome is the improvement of the QoL. The secondary outcomes are improvement of health-related parameters such as Mediterranean diet adherence, physical activity level, flexibility, muscular fitness, fatigue, cardiorespiratory fitness (estimated maximal oxygen uptake), echocardiographic parameters, heart rate variability (average of the standard deviations of all 5 min normal to normal intervals (ASDNN/5 min) and 24 h very low and low frequency), and metabolic, endocrine, and inflammatory serum biomarkers (glycemia, insulin resistance, progesterone, testosterone, and high-sensitivity C-reactive protein). Discussion This trial aims to evaluate if supervised exercise may improve QoL and health-related factors of BC survivors with a high risk of recurrence. Findings from this project could provide knowledge improvement in the field of exercise oncology through the participation of a multidisciplinary team that will provide a coordinated program of cancer care to improve healthcare quality, improve prognosis, increase survival times and QoL, and reduce the risk of BC recurrence. Trial registration ClinicalTrials.gov NCT04818359 . Retrospectively registered on March 26, 2021

Abstract The Mississippi River System (MRS) is one of the most important commercial routes in the world and its navigability is critical for anticipating potential disruptions in the global supply chain. Here we show that the navigability of the MRS has reduced since 1963, especially in the lower part of the basin. Based on analyses of daily stage time series and the associated conditions for navigation, we find that high rather than low water levels are the main culprits for the observed navigability issues. Moreover, not only have the navigable days decreased, but navigation without operational restrictions has also become more fragmented. Our findings provide basic information toward the development of strategies to mitigate potential negative effects in the U.S. navigation sector.

Abstract River flooding has large societal and economic impacts across Africa. Despite the importance of this topic, little is known about the main flood generating mechanisms in Africa. This study is based on 13,815 flood events that occurred between 1981 and 2018 in 529 catchments. These flood events are classified to identify the different flood drivers: excess rains, long rains and short rains. Out of them, excess rains on saturated soils in Western Africa, and long rains for catchments in Northern and Southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. The aridity index is strongly related to the spatial repartition of the different flood generating processes showing the climatic controls on floods. Few significant changes were detected in the relative importance of these drivers over time, but the rather short time series available prevent a robust assessment of flood driver changes in most catchments. The major implication of these results is to underline the importance of soil moisture dynamics, in addition to rainfall, to analyze the evolution of flood hazards in Africa.

Coffee, the most globally prevalent beverage, entails environmental and economic consequences due to the high content of toxic compounds in leftover from brewing. Waste-to-energy technologies propose recovering energy from solid or liquid wastes via various treatment methods. This study investigates the environmental performance of the spent coffee ground application in pellet production as a valuable bio-product by life cycle assessment. System boundaries comprise four stages: (1) spent coffee ground collection; (2) pellet production; (3) thermal energy generation; and (4) waste disposal. The LCA model is interpreted by ReCipe 2016 in both midpoint and endpoint levels per 1 MJ of thermal energy as functional unit. Alternative waste-to-energy approaches into transesterification, pyrolysis, and direct combustion of spent coffee ground are also assessed. The impact assessment shows that the pelletizing process significantly contributes to all impact categories except ozone formation and terrestrial ecotoxicity, which is influenced primarily by the transportation process in the spent coffee ground collection stage. The spent coffee ground pellet can lead to climate change mitigation, and reduction in freshwater eutrophication and use of resources due to credits from ash disposed to landfill.Biodiesel is the worst potential option among alternative scenarios, while pellet production and bio-oil demonstrate the most favorable environmental profiles for all impact categories. Damage assessment indicates that pellet production and alternative scenarios mainly influence human health.

Urbanization has a strong signal on the hydrologic cycle, leading to reduced infiltration and faster and larger runoff. However, the detailed analysis of the contribution of urbanization to different quantiles of discharge is still lacking; particularly, less is known in watersheds that have been experiencing such large and rapid urbanization as those in China. Here, we focus on the Wenyu watershed, a rapidly urbanizing basin located in the Beijing metropolitan area. Using a statistical attribution framework, we examine the hydrological response to the increasing urbanization across a wide range of discharge quantiles, from low to high flows; moreover, we perform analyses at the seasonal scale to capture differences in the physical processes at play during the year. Results indicate that the selected GAMLSS (generalized additive models for location, scale, and shape) models can capture well the variability in streamflow in this highly urbanized basin, with the average Spearman correlation coefficients between observations and the median of the fitted models of 0.84, 0.79, and 0.81 in spring, summer, and winter, respectively. Overall, urbanization played a different role in the different seasons and discharge quantiles. More specifically, we find its strongest impact to be in winter and spring, and for low and median quantiles. The role of precipitation is the strongest in summer, and it increases as we move toward the upper tail of the discharge distribution, especially above the 55th percentile, for which precipitation is selected as the only important predictor. Recycled water, on the other hand, tends to play a more dominant role in winter and spring. HIGHLIGHTS Urbanization has a strong signal on the hydrologic cycle; however, less is known in watersheds that have been experiencing such large and rapid urbanization. In this research, we focus on a fast urbanizing basin, and the hydrological responses to the increasing urbanization have been examined.; The quantification of the impacts of drivers for different discharge quantiles and seasons has been examined.; Urbanization plays a decreasing role towards the upper tail of the distribution.;

Abstract Flooding is one of the most devastating natural disasters causing significant economic losses. One of the dominant drivers of flood losses is heavy precipitation, with other contributing factors such as built environments and socio‐economic conditions superimposed to it. To better understand the risk profile associated with this hazard, we develop probabilistic models to quantify the future likelihood of fluvial flood‐related property damage exceeding a critical threshold (i.e., high property damage) at the state level across the conterminous United States. The model is conditioned on indicators representing heavy precipitation amount and frequency derived from observed and downscaled precipitation. The likelihood of high property damage is estimated from the conditional probability distribution of annual total property damage, which is derived from the joint probability of the property damage and heavy precipitation indicators. Our results indicate an increase in the probability of high property damage (i.e., exceedance of 70th percentile of observed annual property damage for each state) in the future. Higher probability of high property damage is projected to be clustered in the states across the western and south‐western United States, and parts of the U.S. Northwest and the northern Rockies and Plains. Depending on the state, the mean annual probability of high property damage in these regions could range from 38% to 80% and from 46% to 95% at the end of the century (2090s) under RCP4.5 and RCP8.5 scenarios, respectively. This is equivalent to 20%–40% increase in the probability compared to the historical period 1996–2005. Results show that uncertainty in the projected probability of high property damage ranges from 14% to 35% across the states. The spatio‐temporal variability of the uncertainty across the states and three future decades (i.e., 2050s, 2070s, and 2090s) exhibits nonstationarity, which is driven by the uncertainty associated with the probabilistic prediction models and climate change scenarios.