Human-driven sea-level rise has tripled the frequency of coastal sea-level extremes since 1900

Human-driven sea-level rise has tripled the frequency of coastal sea-level extremes since 1900 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Physical Sciences - Article Human-driven sea-level rise has tripled the frequency of coastal sea-level extremes since 1900 Sönke Dangendorf, Qiang Sun, Pravin Maduwantha Mahanthe Gamage, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7491013/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Coastal flooding events are escalating worldwide, yet the role of human-driven sea-level rise remains poorly constrained. Here we provide the first global detection and attribution of changes in extreme sea-level (ESL) frequency since 1900, combining tide gauge records with historical and single-forcing experiments from the Coupled Model Intercomparison Project (CMIP5). We show that relative sea-level (RSL) rise, driven primarily by anthropogenic forcing since the 1960s, has already transformed the likelihood of historically rare extremes. Globally, the median frequency of a historical 1-in-100-year ESL event has increased sixfold, with human-driven forcing alone tripling the likelihood of such events. Natural variability still modulates regional patterns but has become secondary along most coastlines. These findings provide direct, observation-based evidence that climate change has already reshaped coastal flood hazard, underscoring the urgency of integrating attribution science into coastal adaptation, risk management, and policy frameworks. Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Earth and environmental sciences/Climate sciences/Ocean sciences/Physical oceanography Earth and environmental sciences/Natural hazards Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Coastal communities worldwide are increasingly threatened by ESL events, which drive flooding, infrastructure damage, and ecosystem loss 1 . More than 680 million people live in low-lying coastal zones, where accelerating RSL rise poses growing challenges for adaptation and planning 2,3 . Historically rare events, once expected only once per century (1-in-100-years, or 1% chance of occurrence in any given year), are projected to occur annually in many regions by 2100 under high-emission scenarios 1,4-6 . Yet despite these stark projections, a fundamental question remains unanswered: How much has human-driven climate change already altered the frequency of ESLs? ESLs occur when storm surges, tides, and RSL anomalies coincide, and are influenced by a variety of factors such as natural climate variability (e.g., El Niño–Southern Oscillation, volcanic activity) 7,8 and anthropogenic forcing (e.g., greenhouse gas emissions, aerosols) 9,10 . It is generally well established that observed trends in ESLs can largely be explained by underlying changes in RSL 11-13 with few regions showing coherent additional trends associated with storm surges 10,14 or tides 15 . However, knowledge about the anthropogenic contribution to local RSL and ESL changes remains rudimentary 6 . Past studies have quantified the fingerprints of anthropogenic forcing in individual sea-level components, such as thermosteric sea level 16,17 and glacier mass loss 18 . Others have attributed anthropogenic impacts on sea-level changes more comprehensively across all components, including ice sheets and terrestrial water storage (TWS) changes, but only for the global mean 19 or in isolated regions such as the Indo-Pacific 20 . These studies indicate that the anthropogenic contribution to observed GMSL rise since 1900 21,22 has increased from approximately 15% before 1950 to more than 70% since 1970 19 . However, except for the attribution of damages from flooding caused by Hurricane Sandy in the New York region 9 , comprehensive assessments of the anthropogenic signal at the local level that include ESLs are still lacking. Consequently, the Intergovernmental Panel on Climate Change (IPCC) 6 concluded in their 6 th Assessment Report (AR6) with medium confidence that the link between GMSL, RSL and ESL changes implies at least a partial role of anthropogenic forcing, but that a reconciliation of regional variations in these changes would not yet be possible. Here we address this gap using a global detection and attribution analysis that integrates a globally distributed set of tide gauge records 23,24 , historical and single-forcing experiments from CMIP5 25 (for sterodynamic, glacier, and ice sheet contributions), vertical land motion (VLM) 26 and TWS 27 (representing the sum of water impoundment behind dams 28 , groundwater extraction 29 , and changes in the hydrological cycle 30 ) ( Methods ). By disentangling anthropogenic (as the sum of greenhouse gases, aerosols, land use changes and ozone) and natural (volcanic, solar irradiance) contributions to local RSL change and quantifying their impact on ESL return periods, we provide a first-of-its-kind global assessment of human influence on coastal flood hazard. Detection and Attribution of RSL We first assess the performance of historical CMIP5 RSL simulations using 149 long (>50 years) tide gauge records from the Permanent Service for Mean Sea Level (PSMSL) 23 that are within 300 km of a CMIP5 model ocean grid point ( Methods ). From the CMIP5 database we select the 10 models ( Extended Data Figure 1 ) with historical and single-forcing simulations allowing for the isolation of anthropogenic and natural forcings. CMIP5 provides global mean thermosteric sea level and associated dynamic changes as direct outputs, while barystatic changes in ice sheet and glacier mass balance as well as inverse barometer contributions are calculated offline with the associated regional climate forcings ( Methods ). As noted in ref. 19 and ref. 31, contributions from Northern Hemisphere ice sheets and glaciers are well reconstructed by CMIP5 models after 1950 but show significant differences to observational reconstructions before ( Extended Data Figure 2 ). This has been explained by the lack of early-20 th century warming in the Northern Hemisphere in CMIP5 models compared to observations, which has previously been linked to out-of-phase climate-internal variability 32,33 and/or too negative radiative forcing from aerosols 34 counterbalancing warming from greenhouse gases. We follow ref. 19 and ref. 31 and adjust historical simulations with a correction function before 1950 ( Methods ). To accommodate the different interpretations of the lack of Northern Hemisphere warming in CMIP5 models, we calculate three test cases: Case 1 attributes the mismatch entirely to natural variability (100% natural forcing); Case 2 assumes equal contributions from internal variability and excessive aerosol cooling (50% natural, 50% anthropogenic); Case 3 follows ref. 34 and attributes 100% to anthropogenic forcing. Throughout the paper we report numbers for case 2 with case 1 and case 3 given in parenthesis for results that include the pre-1950 period. All barystatic contributions are localized via their associated fingerprints of Gravitation, Rotation, and Deformation (GRD) 35 . VLM and TWS are added separately but are not further categorized into anthropogenic and natural contributions (see Methods for further details). Tide gauge observations reveal, on average, positive trends of 1.49 mm yr -1 around the world’s coastlines with a spatial spread (95% confidence interval) ranging from -2.56 mm yr -1 to 7.11 mm yr -1 ( SI Table 1 ). Historical CMIP5 simulations combined with VLM and TWS estimates agree well with the observations (1.42 mm yr -1 [-3.07 mm yr -1 ; 6.45 mm yr -1 ]), with a median root mean squared (RMS) difference in the linear trends over their overlapping periods of 0.70 mm yr -1 [0.39 mm yr -1 ; 1.17 mm yr -1 ] across all 10 models ( Figure 1a-g , and SI Table 1 for individual sites). This performance improves when comparing observations to the CMIP5 ensemble mean, which is less affected by internal variability and generally more reflective of the response to common forcing that all models share (RMS = 0.41 mm yr -1 , Figure 1g ). Indeed, the linear trends from observations and ensemble-mean CMIP5 model historical simulations agree across all sites within the associated error bars. Breaking down the modelled historical trends into the different contributions since 1900 ( Figure 1h, SI Table 1 ) shows that anthropogenic forcing is, on average, the most important forcing across all three cases with a median contribution across stations of 0.75 mm yr -1 and a spread from 0.31 mm yr -1 to 1.19 mm yr -1 in case 2 (case 1: 0.70 mm yr -1 [0.31 mm yr -1 , 1.12 mm yr -1 ]; case 3: 0.80 mm yr -1 [0.28 mm yr -1 ;1.29 mm yr -1 ]). Natural forcing has also contributed significantly with a median across stations of 0.48 mm yr -1 and a spatial spread from -0.18 mm yr -1 to 0.91 mm yr -1 (case 1: 0.51 mm yr -1 [-0.21 mm yr -1 , 1.08 mm yr -1 ]; case 3: 0.45 mm yr -1 [-0.15 mm yr -1 ;0.75 mm yr -1 ]). In contrast, TWS has contributed, on average, negatively to the observed RSL trends (-0.16 mm yr -1 [-0.42 mm yr -1 ; 0.02 mm yr -1 ]), which can be explained by the vicinity of many tide gauges to major reservoirs and their associated GRD fingerprints 36,37 . While VLM generally had a smaller average impact than anthropogenic and natural forcings (median of 0.30 mm yr -1 ), its spread is multiple times larger than that of any other contribution with extreme cases ranging from -12.21 mm yr -1 at Juneau, Alaska to 13.84 mm yr -1 at Fort Phra Chulachomklao near Bangkok, Thailand. At Juneau, the VLM signal is dominated by uplift related to Glacial Isostatic Adjustment ( Figure 1a ) 38 (Larsen et al., 2004), while at Bangkok rapidly accelerated groundwater withdrawals since the 1960s have caused strong nonlinear subsidence ( Figure 1d ) 26,39 . When assessing the shorter period since 1970, numbers change primarily for the anthropogenic and natural forcings ( Figure 1h ). While the anthropogenic contribution increases on average to 0.87 mm yr -1 [0.13 mm yr -1 ; 1.95 mm yr -1 ], the contribution of natural forcing decreases to 0.30 mm yr -1 [-0.27 mm yr -1 ; 0.69 mm yr -1 ]. This is generally consistent with the findings for GMSL that also indicate an increasing anthropogenic contribution over the 20 th century with a particularly strong imprint after 1970 19 . For TWS and VLM, medians across sites remain nearly unchanged, but the spread for VLM increases further (-12.19 mm yr -1 ; 18.19 mm yr -1 ), indicating more extreme VLM cases in the 2 nd half of the 20 th century, likely due to increased fluid withdrawals 26 . Detection and Attribution of ESL Having disentangled the different forcings of local RSL changes over the 20 th century, we next extend the attribution framework to ESLs. To do so, we select 124 out of the initial 149 PSMSL records from the Global Extreme Sea Level Analysis v3 (GESLAv3) database 24 covering at least 15 years of hourly tide gauge data. At each location, sea levels are detrended to present-day levels using a one-year moving average. We then estimate return periods and levels using a peak-over-threshold approach, selecting events above the 99.7 th percentile with a seven-day de-clustering window 40,41 . A Generalized Pareto Distribution (GPD) is fitted to the resulting ESL samples. Long-term changes in return levels are assessed by vertically shifting the return level curve using observed and simulated RSL changes, following a similar approach as used in IPCC AR6 for future ESLs 6 . Figure 2 illustrates this method for Sandy Hook, New Jersey, and Wellington, New Zealand. Sandy Hook’s GPD shape is convex (manifested in a long tail in the distribution), reflecting high ESL variability characterized by a mix of tropical (Hurricanes) and extra-tropical (Nor’easters) storms. A simulated historical 0.44 m RSL rise since 1900 has shifted the 1-in-100-year event to a 1-in-15-year event by 2005 ( SI Table 2 ), an amplification factor of 6.7 (i.e., an almost sevenfold increase in the likelihood of occurrence; calculated as , where is the amplification factor, and and are the return periods in 1900 and 2005, respectively). In contrast, Wellington’s concave GPD shape reflects a short tail in the distribution and thus lower ESL variability. With less than half of the RSL rise at Sandy Hook (i.e., ~0.2 cm), the 1-in-100-year event in 1900 became almost a 2-in-1-year event by 2005 ( SI Table 2 ), an amplification factor of 181.7. Thus, the shape of the distribution has a strong control over the changes in return periods relative to RSL 4 . Decomposing the ESL return period changes since 1900 into individual forcings shows subsidence as the main driver at Sandy Hook, having reduced the return period to 1-in-30-years, while anthropogenic and natural forcings shifted it in case 2 to 1-in-69-years (anthropogenic, case 1: 1-in-72-years; case 3: 1-in-66-years) and 1-in-75-years (natural, case 1: 1-in-72-years; case 3: 1-in-78-years), respectively. At Wellington, anthropogenic and natural forcings each shortened the return period, in case 2 to 1-in-2-years (anthropogenic, case 1: 1-in-3-years; case 3: 1-in-2-years) and 1-in-6-years (natural, case 1: 1-in-4-years; case 3: 1-in-9-years), respectively, with subsidence contributing only comparatively modestly (1-in-46-years). TWS had a negligible impact on both sites ( SI Table 2 ). Globally, a similar picture emerges ( Figure 3 ). In all three cases, at 58 out of the 124 analyzed sites the 1-in-100-year event from 1900 increased in likelihood to become at least a 1-in-10-year event in 2005, translating to an amplification factor of 10 or even larger. Only 18 sites (located in Northern Europe, North America, and Japan) experienced a reduction in the frequency of extremes, due to either glacial isostatic adjustment and/or earthquake-related uplift counterbalancing the observed anthropogenic and natural increases in ESLs. On average (median across all 124 sites), the 1-in-100-year event sextupled over the 1900 to 2005 period to a 1-in-16-year event ( Figure 3b ). Anthropogenic forcing has been the most important forcing factor behind these changes, on average, tripling the occurrences of the 1-in-100-year event to a 1-in-35-year (case 1: 1-in-35-year; case 3: 1-in-32.5-year) event ( Figure 3c ). Natural forcing has also contributed positively, doubling the likelihood of the 1-in-100-year to a 1-in-46.5-year (case 1: 1-in-42-year; case 3: 1-in-48-years) event. VLM has induced the most extreme changes in ESLs locally. For instance, at Manila, Philippines, rapidly accelerated groundwater withdrawals since the 1960s have increased local RSL by ~0.6 m leading to a 600-fold increase in the frequency of a 1-in-100-year event (now occurring more than 3 times a year). However, the median impact across sites has been less than that of anthropogenic and natural forcings; VLM alone has increased the likelihood of the 1-in-100-year event from 1900 to a 1-in-66-year event in 2005. At the same time, TWS has on average increased the ESL return periods slightly (i.e., made them less likely) from a 1-in100 year to a 1-in-104-year event ( Figure 3c ). Importantly, the forcing factors have been nonstationary over the 20 th century, leading to varying degrees of influence of RSL change on the associated ESL return periods over time ( Figure 4 ). While natural and anthropogenic forcings were equally important contributors to observed RSL rates until the mid-1960s with peak natural contributions in the 1930s, anthropogenically driven RSL change has steadily increased at most sites, becoming the most important forcing factor since the mid-1960s ( Figure 4a ). Accordingly, most of the naturally forced ESL return period changes occurred before the 1960s, while the anthropogenic forcing became the dominant driver beginning in the late 1970s leading to an accelerated increase in the likelihood of extreme events (i.e., decreasing their return periods) ( Figure 4b ). Discussion & Conclusions Our findings reveal that both global and local processes shape RSL changes and their imprint on ESLs, with anthropogenic forcing now exerting a dominant and growing influence not only on GMSL but also on RSL and the frequency of ESL events. While our analysis isolates RSL-driven trends, we have not explicitly accounted for possible future shifts in storm surge climate 10,14 or tides 15,42,43 . Testing for additional ESL drivers beyond RSL, we compared the trends in the 99.7 th percentile sea-level threshold, computed over a 15-year moving window, with those in RSL. Significant differences emerged at only three of 124 sites (Wilmington, Esbjerg, Amderma) ( Extended Data Figure 3g ), each showing documented long-term tidal range changes 15,42,44,45 without corresponding storm surge trends. This confirms that, at most sites, RSL is the primary driver of ESL changes, consistent with previous studies 11 . Nevertheless, isolated regional studies have detected storm surge changes of comparable magnitude to individual RSL components in Northern Europe 10 and North America 14 , and while their attribution remains limited by signal-to-noise constraints, such changes can alter ESL return periods on planning-relevant timescales. Refining present-day ESL statistics will require careful accounting for such non-RSL-related non-stationarities and their anthropogenic or natural drivers. The magnitude of observed return-period changes is also sensitive to the underlying extreme-value methodology and record length 41,46,47 . Distributions computed for sites with shorter records, particularly in low latitudes where tropical-cyclone storm tides are under-sampled 48,49 , can be reshaped by individual record-breaking events, influencing derived non-stationary return periods. Despite these caveats, our analysis fills a key gap in detection and attribution research for coastal ESLs. We show that RSL rise since 1900 significantly shortened ESL frequency: at nearly half the sites, a historical 1-in-100-year event now occurs at least once per decade, and the global median frequency has increased sixfold. Anthropogenic forcing alone has tripled the likelihood of such extremes, with the strongest influence since the mid-1960s, while natural forcings were more influential in the early 20th century. This conclusion holds even under alternative attributions of the underestimated pre-1950 ice-mass loss in CMIP5 models. These results provide robust, observation-based evidence that climate change has already reshaped global coastal flood hazard. The transformation is not a distant prospect, it is underway, underscoring the need for urgent adaptation and sustained mitigation to limit future risk escalation. These findings also carry implications beyond science and adaptation planning. By directly linking observed increases in ESL frequency to anthropogenic forcing, our results provide a quantitative evidentiary basis relevant to climate litigation and loss and damage claims. Courts increasingly require robust attribution studies to establish causality between greenhouse gas emissions and specific physical impacts 50 . The detection of anthropogenic signals in coastal flood hazard across multiple sites and using observational data strengthens the chain of evidence connecting emissions from identified sources to measurable harm in vulnerable communities. As climate litigation expands globally 51 (Setzer & Higham, 2022), especially in low-lying coastal regions, such attribution analyses may inform both legal arguments and the valuation of damages. Methods Tide Gauge Data. To assess changes in observed RSL, we select 149 tide gauge records from the PSMSL database 23 , using annual averages from sites that provide at least 50 years of data and are located within 300 km of the nearest CMIP5 model ocean grid point. Consequently, sites in semi-enclosed basins not adequately resolved in CMIP5 models, such as the Mediterranean and Baltic Seas, are excluded. From these 149 PSMSL sites, we further select 124 locations that also provide at least 15 years of hourly records in the GESLA v3 database 24 . Although the attribution analysis is only performed over 1900 to 2005, we consider hourly GESLA records till the end date of the time series in order to maximize the number of extreme events and sites that fulfill the minimum criteria of 15-year data availability. Climate Model Data . For modelled RSL, we use simulations from CMIP5 25 ( Extended Data Table 1 ), including both historical and single-forcing experiments (natural and anthropogenic), depending on availability. CMIP6 has not been used here given the lack of glacier model simulations for historical and single-forcing experiments. Although many models offer multiple realizations per forcing experiment, we select only one realization per model to ensure equal weighting across models. Where single-forcing simulations are not available, they are estimated by differencing historical and other single-forcing runs under the assumption of linear forcing response 19 , e.g., Anthropogenic = Historical – Natural . CMIP5-based RSL is composed of three components: sterodynamic sea level, barystatic sea level (from glaciers and ice sheets), and the inverted barometer effect. Sterodynamic contributions are estimated by combining local ocean dynamics (zos) with global mean thermosteric sea level (zostoga). Model drift in both variables is removed using a quadratic fit to pre-industrial control simulations 52 . Following ref. 19, we also apply a 0.1 mm yr⁻¹ linear correction to account for ocean disequilibrium prior to 1850, consistent with geological estimates 53 . Glacier-related barystatic contributions are derived using simulations from ref. 18, which compute mass balance across 19 glacier regions based on local climate conditions. These estimates omit very small and vanished glaciers that are not in the global glacier inventories, and thus we add a global correction of 0.05 mm yr⁻¹ 54 , treated as entirely anthropogenic. Glacier mass loss is regionalized using the GRD fingerprints associated with each glacier region 35 . For unlocalized mass loss from missing and vanished glaciers, we apply the 20 th -century average GRD pattern from ref. 27. Ice sheet contributions are parameterized from CMIP5 outputs. For the Greenland ice-sheet surface mass balance (SMB) contribution, the regional climate model MARv3.510 was forced with three CMIP5 models (MIROC5-historical over the period 1900–2005, NorESM1-historical over the period 1950–2005 and CanESM1-historical over the period 1950–2005). Given the large computational requirements of MAR, the output from these simulations is used to establish a statistical relationship between CMIP5 annual snowfall over Greenland, atmospheric summer temperature at 600 hPa and the Greenland surface mass balance 55 , resulting in: where TT = average June–July–August air temperature (CMIP5 variable TA) at 600 hPa (20–70 W; 60–85 N) and SF = annual snowfall in mm yr -1 (CMIP5 variable PRSN, 20–70 W; 65–80 N). For Antarctica, surface mass balance is estimated by subtracting evaporation (evspsbl) from precipitation (pr) over the Antarctic, following ref. 19. Annual changes are integrated and converted to global sea-level equivalents, then regionalized using the associated GRD fingerprints. Consistent with prior work 19,31 , both simulated glacier and ice sheet mass loss agree with observations after the 1950s but underestimate earlier trends. This underestimation is attributed to missing internal variability 32,33 and/or overestimated aerosol cooling 34,56 . We follow ref. 19 and ref. 31 and fit a smooth correction function to the pre-1950 mismatch ( Extended Data Fig. 3 ). As no forcing breakdown is possible for this mismatch, we assume three different test cases, in which the fraction of natural and anthropogenic forcings are varied for the correction function (case 1: 100% natural, case 2: 50% natural and 50% anthropogenic, case 3: 100% anthropogenic). These corrections are regionalized using observed average GRD fingerprints from ref. 27. Inverted barometer effects are estimated from CMIP5 sea-level pressure (psl) using the hydrostatic relationship 57 , assuming a 1 cm sea-level rise per 1 hPa drop in local pressure. While globally neutral, this effect can significantly alter local RSL 58 . All CMIP5 outputs are sampled at the nearest neighboring grid point to a tide gauge and smoothed to reflect climate-related variability at timescales longer than 30 years using Singular Spectrum Analysis with an embedding dimension of 15 years 59 . Model ensemble statistics are provided as medians across the 10 models, with inter-model spread given by the standard deviation. TWS Contribution . TWS changes, due to dam impoundment 28 , groundwater withdrawal 29 , and hydrological variability 30 , are not included in CMIP5. We therefore adopt observational TWS estimates from the compilation in ref. 27, using a 100-member ensemble to sample the associated uncertainties over time. No forcing breakdown is applied for this term. VLM Contribution . VLM is presently inferred primarily from the Global Navigation Satellite System (GNSS) or Interferometric Synthetic Aperture Radar (InSAR) data, although these are limited in spatial and temporal coverage 60,61 . To cover the entire 20 th century and include nonlinearities in VLM, we use estimates from ref. 26, in which VLM is calculated as the residuals between local tide gauges and nearby sea-level reconstructions 22 , resolving decadal and nonlinear signals with ~1 mm yr⁻¹ accuracy. At 16 sites (Aburatsubo, Wajima, Hosojima, Onahama, Tonoura, Mera, Manila, Legaspi, Ko-Lak, Fort Phra Chulachomklao, Galveston, Prince Rupert, Port Isabel, Grand Isle, Lyttleton, and Yakutat), we consider nonlinear VLM effects primarily related to time-varying fluid withdrawals and/or earthquakes and volcanic activity. At those sites a Gaussian Process is used to filter out decadal and longer signals of VLM. For sites at which tide gauge recordings start later in the 20 th century, we assume (depending on the site) that the nonlinear VLM converges to either the local GIA signal (e.g., at Fort Phra Chulachomklao near Bangkok, Figure 1d ) or to the associated local linear VLM rate before observations become available. At the other 133 locations a linear least squares fit is estimated over the entire observational period. Extreme Value Statistics . We follow the best practices described in ref. 40 and applied in most recent IPCC reports 2,6 to select and model ESLs from observations. First, the hourly sea-level records are detrended to present-day (i.e., adjusting historical values to reflect present-day conditions without a trend) using a moving one-year average filter. Then a peak-over-threshold approach is applied to select ESLs that are larger than the 99.7 th percentile of the entire detrended hourly record. The detected events are de-clustered using a de-clustering timescale of seven days that ensures that only independent events enter the ESL sample. A GPD distribution is then fitted to the resulting sample of threshold exceedances to estimate associated return periods and levels. To test non-stationarity in different GPD parameters, the same analysis is also performed, where possible, using a 15-year running window. We note that systematic long-term changes in distribution parameters were primarily detected for the location parameter (i.e., the threshold, the 99.7 th percentile), while changes in shape and scale are limited to a few isolated sites ( Extended Data Fig. 4 ). Declarations Data availability . The tide gauge data used in this study is publicly available from the Permanent Service of Mean Sea Level (https://www.psmsl.org/), while the GRD fingerprints and VLM estimates at individual locations are accessible from the ref. 27. All data required to perform the analysis is available via the ZENODO repository 10.5281/zenodo.16995841. Code availability . All code required to perform the analysis is available via the ZENODO repository 10.5281/zenodo.16995841. Acknowledgements: S.D., P.M., and T.W. acknowledge the National Science Foundation grant ICER-2103754 (as part of the Megalopolitan Coastal Transformation Hub). S.D. acknowledges NASA grant 80NSSC24K1529 and David and Jane Flowerree for their endowment funds. T.W. acknowledges NASA grant 80NSSC24K1527. Correspondence : Correspondence and requests should for materials should be addressed to S.D. Author contributions: S.D. designed and performed the research and wrote the first draft of the paper. Q.S. developed the CMIP5 sea-level database and handled the GESLA dataset. P.M. and T.W. supported the coding of the GPD analysis. A.B.A.S. and B.M. provided the Greenland ice sheet and glacier data for CMIP5 models. 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(2018). Twentieth-century contribution to sea-level rise from uncharted glaciers. Nature , 563 (7732), 551-554. Fettweis, X., Franco, B., Tedesco, M., Van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., & Gallée, H. (2013). Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR. The Cryosphere , 7 (2), 469-489. Rotstayn, L. D., Collier, M. A., Shindell, D. T., & Boucher, O. (2015). Why does aerosol forcing control historical global-mean surface temperature change in CMIP5 models?. Journal of Climate , 28 (17), 6608-6625. Wunsch, C., & Stammer, D. (1997). Atmospheric loading and the oceanic “inverted barometer” effect. Reviews of Geophysics , 35 (1), 79-107. Piecuch, C. G., Thompson, P. R., & Donohue, K. A. (2016). Air pressure effects on sea level changes during the twentieth century. Journal of Geophysical Research: Oceans , 121 (10), 7917-7930. Moore, J. C., Grinsted, A., & Jevrejeva, S. (2005). New tools for analyzing time series relationships and trends. Eos, Transactions American Geophysical Union , 86 (24), 226-232. Wöppelmann, G., & Marcos, M. (2016). Vertical land motion as a key to understanding sea level change and variability. Reviews of Geophysics , 54 (1), 64-92. Shirzaei, M., Freymueller, J., Törnqvist, T. E., Galloway, D. L., Dura, T., & Minderhoud, P. S. (2021). Measuring, modelling and projecting coastal land subsidence. Nature Reviews Earth & Environment , 2 (1), 40-58. Additional Declarations There is NO Competing Interest. Supplementary Files SITables.xlsx SI Tables 1 and 2 ExtendedData.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7491013","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":509889392,"identity":"ef91de0a-e671-4ec6-a8e7-477cb399e220","order_by":0,"name":"Sönke Dangendorf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYFCCBDDJ2MDA+ODABwYGGYgoG1FamA0Pzkhg4CFJi/FhHmK0yLcnP3v4peKObL/0YYbDtj8O8/DPbj7A8KHsME4tBmeemRvLnHlmPLMvmeFwTsJhHok7xxIYZ5zDo0UiwUxasu1w4oYz/AfAWhhu5Bgw87bh1iI/I/2btOQ/kBZmhsMWQC3yN/I/MP/FowVoppnkxwaoFgagFoMbOQzMjHi0GJx5UybNcOyw8cweZoaDPWnpPIY30gwO9pxLx+2w9vRtkj9qDsv28zAzf/hhYy0ndyP54YMfZda4HQYEzDzoIgfwqgcCxh+EVIyCUTAKRsHIBgDMLV2APKo1JgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3679-5234","institution":"Tulane University","correspondingAuthor":true,"prefix":"","firstName":"Sönke","middleName":"","lastName":"Dangendorf","suffix":""},{"id":509889393,"identity":"098a6c87-2b1e-4aca-85c1-9faffd1ce960","order_by":1,"name":"Qiang Sun","email":"","orcid":"","institution":"Tulane Universuty","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Sun","suffix":""},{"id":509889394,"identity":"04009ac8-67e7-4d3b-bd1f-bfc01dc385e6","order_by":2,"name":"Pravin Maduwantha Mahanthe Gamage","email":"","orcid":"https://orcid.org/0000-0002-9819-5170","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Pravin","middleName":"Maduwantha Mahanthe","lastName":"Gamage","suffix":""},{"id":509889395,"identity":"40b4bc06-fd00-463c-a9a9-95eaf4d4cc2b","order_by":3,"name":"Thomas Wahl","email":"","orcid":"https://orcid.org/0000-0003-3643-5463","institution":"University of Central Florida, USA","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Wahl","suffix":""},{"id":509889396,"identity":"f3cbc011-b6ae-458d-ace1-699f083f08a1","order_by":4,"name":"Marta Marcos","email":"","orcid":"https://orcid.org/0000-0001-9975-5013","institution":"University of the Balearic Islands","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Marcos","suffix":""},{"id":509889397,"identity":"09cd8825-f4c7-4b3f-8f15-0e3c861a7662","order_by":5,"name":"Ben Marzeion","email":"","orcid":"https://orcid.org/0000-0002-6185-3539","institution":"University of Bremen","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Marzeion","suffix":""},{"id":509889398,"identity":"f05c9d18-83c5-4898-aa3f-5e9390a646b9","order_by":6,"name":"Aimée Slangen","email":"","orcid":"https://orcid.org/0000-0001-6268-6683","institution":"Royal Netherlands Institute for Sea Research","correspondingAuthor":false,"prefix":"","firstName":"Aimée","middleName":"","lastName":"Slangen","suffix":""},{"id":509889399,"identity":"d8b8603e-4e0d-44e5-8162-cdb334933176","order_by":7,"name":"Jerry Mitrovica","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Jerry","middleName":"","lastName":"Mitrovica","suffix":""}],"badges":[],"createdAt":"2025-08-29 18:40:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7491013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7491013/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95524921,"identity":"0cac3e0a-9163-459a-b112-0aa985d1067d","added_by":"auto","created_at":"2025-11-10 10:03:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":304157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAttribution of observed RSL changes using historical and single forcing CMIP5 runs over 1900 to 2005 for case 2\u003c/strong\u003e. Shown are time series of observed and simulated RSL at six example sites of Juneau, Alaska (\u003cstrong\u003ea\u003c/strong\u003e), Galveston, Texas (\u003cstrong\u003eb\u003c/strong\u003e), Alesund, Norway (\u003cstrong\u003ec\u003c/strong\u003e), Fort Phra Chulachomklao near Bangkok, Thailand (\u003cstrong\u003ed\u003c/strong\u003e), Pago Pago, American Samoa (\u003cstrong\u003ee\u003c/strong\u003e), and Port Adelaide, Australia (\u003cstrong\u003ef\u003c/strong\u003e). Shadings represent 2σ confidence intervals for each contribution. (\u003cstrong\u003eg\u003c/strong\u003e) Scatter plot of observed linear tide gauge trends and historical CMIP5 simulations from individual models (IM, grey crosses) and the associated ensemble mean (EM, magenta circles). The dashed line marks the 1:1 line. (\u003cstrong\u003eh\u003c/strong\u003e) Linear trend contributions to RSL \u003cstrong\u003efor\u003c/strong\u003e 1900 to 2005 (shaded bars) and for 1970 to 2005 (shaded and striped bars). Error bars mark the 1σ range across all sites.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7491013/v1/c3f2c262d258825d1762d7c1.png"},{"id":95373874,"identity":"bd9575b1-c978-4bac-b234-81a05c77eb10","added_by":"auto","created_at":"2025-11-07 10:22:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":265953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAttribution of RSL-induced changes in ESL return periods and levels\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e), and (\u003cstrong\u003ec\u003c/strong\u003e) show the GPD distribution fit based on the peaks over the 99.7\u003csup\u003eth\u003c/sup\u003e percentile threshold as a function of the underlying RSL rise at Sandy Hook, New Jersey and Wellington, New Zealand, respectively. The colors indicate the corresponding year of each distribution from 1900 to 2005. The vertical and horizontal arrows mark the shift of the associated return level (vertical) and period (horizontal) of a 1-in-100-year event over time. The amplification factor shown with the horizontal bar is calculated by dividing the return period from 1900 (100-year) through the respective return period in 2005. The corresponding time series, separated into the contributions of individual forcings are shown in (\u003cstrong\u003ec\u003c/strong\u003e), and (\u003cstrong\u003ed\u003c/strong\u003e), respectively. Shadings mark 2σ confidence intervals. All results are presented for case 2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7491013/v1/19de2d9f12cfeb74321a7eea.png"},{"id":95373878,"identity":"8fcca35b-bdd0-49d6-859e-c740b5efd2bd","added_by":"auto","created_at":"2025-11-07 10:22:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in RSL-induced ESL return periods from 1900 to 2005 and their attribution to individual forcings globally for case 2\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Map of the 2005 return period of the historical 1-in-100-year event from 1900. The legend to the right shows the circles corresponding to 10-year return period bins, whereas the numbers to the left and right represent the central year of each bin, e.g., 95 represents all sites that have a return period between 91 and 100 years in 2005. (\u003cstrong\u003eb\u003c/strong\u003e) Histogram of the return periods shown in (\u003cstrong\u003ea\u003c/strong\u003e) using historical CMIP5 simulations + VLM + TWS. (\u003cstrong\u003ec\u003c/strong\u003e) Same as (\u003cstrong\u003eb\u003c/strong\u003e) but broken down into individual forcings. Dashed lines in (\u003cstrong\u003eb\u003c/strong\u003e) and (\u003cstrong\u003ec\u003c/strong\u003e) mark the median across all sites.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7491013/v1/6bde067a3b677e3efe4e8377.png"},{"id":95373875,"identity":"1de44628-146d-4b35-a687-58bf9dc003a2","added_by":"auto","created_at":"2025-11-07 10:22:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-stationary contributions of individual forcings to RSLs and ESLs across all sites for case 2\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Rates of nonlinear RSL changes derived from a nonlinear trend fit using a Singular Spectrum Analysis with an embedding dimension of 15 (filtering out signals larger than 30 years). (\u003cstrong\u003eb\u003c/strong\u003e) Associated changes in the historical (1900) 1-in-100-years return period over time represented as a change in the associated amplification factor. In both plots, thick lines mark the median across all sites, while shadings mark 1σ spread across sites. All results here are presented for case 2. VLM and TWS are excluded due to their average contribution being across sites.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7491013/v1/1b8b858736d0e01124606da7.png"},{"id":95530736,"identity":"dacf2773-387f-4f1b-bf7d-be1c7e73faaa","added_by":"auto","created_at":"2025-11-10 10:21:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1724104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7491013/v1/6f632d73-017f-48f0-aed7-2e2d539546f4.pdf"},{"id":95373876,"identity":"36cba757-2793-4fe0-bcd9-63ddcad7428f","added_by":"auto","created_at":"2025-11-07 10:22:28","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53132,"visible":true,"origin":"","legend":"SI Tables 1 and 2","description":"","filename":"SITables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7491013/v1/c07cd298eba543da86d7fae6.xlsx"},{"id":95373879,"identity":"53d984be-001c-45d9-8339-9f1187a6b76d","added_by":"auto","created_at":"2025-11-07 10:22:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":604647,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-7491013/v1/8ae22afe24ce2eb6af49adb9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Human-driven sea-level rise has tripled the frequency of coastal sea-level extremes since 1900","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoastal communities worldwide are increasingly threatened by ESL events, which drive flooding, infrastructure damage, and ecosystem loss\u003csup\u003e1\u003c/sup\u003e. More than 680 million people live in low-lying coastal zones, where accelerating RSL rise poses growing challenges for adaptation and planning\u003csup\u003e2,3\u003c/sup\u003e. Historically rare events, once expected only once per century (1-in-100-years, or 1% chance of occurrence in any given year), are projected to occur annually in many regions by 2100 under high-emission scenarios\u003csup\u003e1,4-6\u003c/sup\u003e. Yet despite these stark projections, a fundamental question remains unanswered: How much has human-driven climate change already altered the frequency of ESLs?\u003c/p\u003e\n\u003cp\u003eESLs occur when storm surges, tides, and RSL anomalies coincide, and are influenced by a variety of factors such as natural climate variability (e.g., El Niño–Southern Oscillation, volcanic activity)\u003csup\u003e7,8\u003c/sup\u003e and anthropogenic forcing (e.g., greenhouse gas emissions, aerosols)\u003csup\u003e9,10\u003c/sup\u003e. It is generally well established that observed trends in ESLs can largely be explained by underlying changes in RSL\u003csup\u003e11-13\u003c/sup\u003e with few regions showing coherent additional trends associated with storm surges\u003csup\u003e10,14\u003c/sup\u003e or tides\u003csup\u003e15\u003c/sup\u003e. However, knowledge about the anthropogenic contribution to local RSL and ESL changes remains rudimentary\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePast studies have quantified the fingerprints of anthropogenic forcing in individual sea-level components, such as thermosteric sea level\u003csup\u003e16,17\u003c/sup\u003e and glacier mass loss\u003csup\u003e18\u003c/sup\u003e. Others have attributed anthropogenic impacts on sea-level changes more comprehensively across all components, including ice sheets and terrestrial water storage (TWS) changes, but only for\u0026nbsp;the global mean\u003csup\u003e19\u003c/sup\u003e or in isolated regions such as the Indo-Pacific\u003csup\u003e20\u003c/sup\u003e. These studies indicate that the anthropogenic contribution to observed GMSL rise since 1900\u003csup\u003e21,22\u003c/sup\u003e has increased from approximately 15% before 1950 to more than 70% since 1970\u003csup\u003e19\u003c/sup\u003e. However, except for the attribution of damages from flooding caused by Hurricane Sandy in the New York region\u003csup\u003e9\u003c/sup\u003e, comprehensive assessments of the anthropogenic signal at the local level that include ESLs are still lacking. Consequently, the Intergovernmental Panel on Climate Change (IPCC)\u003csup\u003e6\u003c/sup\u003e concluded in their 6\u003csup\u003eth\u003c/sup\u003e Assessment Report (AR6) with medium confidence that the link between GMSL, RSL and ESL changes implies at least a partial role of anthropogenic forcing, but that a reconciliation of regional variations in these changes would not yet be possible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we address this gap using a global detection and attribution analysis that integrates a globally distributed set of tide gauge records\u003csup\u003e23,24\u003c/sup\u003e, historical and single-forcing experiments from CMIP5\u003csup\u003e25\u003c/sup\u003e (for sterodynamic, glacier, and ice sheet contributions), vertical land motion (VLM)\u003csup\u003e26\u003c/sup\u003e and TWS\u003csup\u003e27\u003c/sup\u003e (representing the sum of water impoundment behind dams\u003csup\u003e28\u003c/sup\u003e, groundwater extraction\u003csup\u003e29\u003c/sup\u003e, and changes in the hydrological cycle\u003csup\u003e30\u003c/sup\u003e) (\u003cstrong\u003eMethods\u003c/strong\u003e). By disentangling anthropogenic (as the sum of greenhouse gases, aerosols, land use changes and ozone) and natural (volcanic, solar irradiance) contributions to local RSL change and quantifying their impact on ESL return periods, we provide a first-of-its-kind global assessment of human influence on coastal flood hazard.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection and Attribution of RSL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first assess the performance of historical CMIP5 RSL simulations using 149 long (\u0026gt;50 years) tide gauge records from the Permanent Service for Mean Sea Level (PSMSL)\u003csup\u003e23\u003c/sup\u003e that are within 300 km of a\u0026nbsp;CMIP5 model ocean grid point (\u003cstrong\u003eMethods\u003c/strong\u003e). From the CMIP5 database we select the 10 models (\u003cstrong\u003eExtended Data Figure 1\u003c/strong\u003e) with historical and single-forcing simulations allowing for the isolation of anthropogenic and natural forcings. CMIP5 provides global mean thermosteric sea level and associated dynamic changes as direct outputs, while barystatic changes in ice sheet and glacier mass balance as well as inverse barometer contributions are calculated offline with the associated regional climate forcings (\u003cstrong\u003eMethods\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs noted in ref. 19 and ref. 31, contributions from Northern Hemisphere ice sheets and glaciers are well reconstructed by CMIP5 models after 1950 but show significant differences to observational reconstructions before (\u003cstrong\u003eExtended Data Figure 2\u003c/strong\u003e). This has been explained by the lack of early-20\u003csup\u003eth\u003c/sup\u003e century warming in the Northern Hemisphere in CMIP5 models compared to observations, which has previously been linked to out-of-phase climate-internal variability\u003csup\u003e32,33\u003c/sup\u003e and/or too negative radiative forcing from aerosols\u003csup\u003e34\u003c/sup\u003e counterbalancing warming from greenhouse gases. We follow ref. 19 and ref. 31 and adjust historical simulations with a correction function before 1950 (\u003cstrong\u003eMethods\u003c/strong\u003e). To accommodate the different interpretations of the lack of Northern Hemisphere warming in CMIP5 models, we calculate three test cases: Case 1 attributes the mismatch entirely to natural variability (100% natural forcing); Case 2 assumes equal contributions from internal variability and excessive aerosol cooling (50% natural, 50% anthropogenic); Case 3 follows ref. 34 and attributes 100% to anthropogenic forcing. Throughout the paper we report numbers for case 2 with case 1 and case 3 given in parenthesis for results that include the pre-1950 period. All barystatic contributions are localized via their associated fingerprints of Gravitation, Rotation, and Deformation (GRD)\u003csup\u003e35\u003c/sup\u003e. VLM and TWS are added separately but are not further categorized into anthropogenic and natural contributions (see \u003cstrong\u003eMethods\u003c/strong\u003e for further details).\u003c/p\u003e\n\u003cp\u003eTide gauge observations reveal, on average, positive trends of 1.49 mm yr\u003csup\u003e-1\u003c/sup\u003e around the world’s coastlines with a spatial spread (95% confidence interval) ranging from -2.56 mm yr\u003csup\u003e-1\u003c/sup\u003e to 7.11 mm yr\u003csup\u003e-1\u003c/sup\u003e (\u003cstrong\u003eSI Table 1\u003c/strong\u003e). Historical CMIP5 simulations combined with VLM and TWS estimates agree well with the observations (1.42 mm yr\u003csup\u003e-1\u003c/sup\u003e [-3.07 mm yr\u003csup\u003e-1\u003c/sup\u003e; 6.45 mm yr\u003csup\u003e-1\u003c/sup\u003e]), with a median root mean squared (RMS) difference in the linear trends over their overlapping periods of 0.70 mm yr\u003csup\u003e-1\u003c/sup\u003e [0.39 mm yr\u003csup\u003e-1\u003c/sup\u003e; 1.17 mm yr\u003csup\u003e-1\u003c/sup\u003e] across all 10 models (\u003cstrong\u003eFigure 1a-g\u003c/strong\u003e, and \u003cstrong\u003eSI Table 1\u0026nbsp;\u003c/strong\u003efor individual sites). This performance improves when comparing observations to the CMIP5 ensemble mean, which is less affected by internal variability and generally more reflective of the response to common forcing that all models share (RMS = 0.41 mm yr\u003csup\u003e-1\u003c/sup\u003e, \u003cstrong\u003eFigure 1g\u003c/strong\u003e). Indeed, the linear trends from observations and ensemble-mean CMIP5 model historical simulations agree across all sites within the associated error bars. Breaking down the modelled historical trends into the different contributions since 1900 (\u003cstrong\u003eFigure 1h, SI Table 1\u003c/strong\u003e) shows that anthropogenic forcing is, on average, the most important forcing across all three cases with a median contribution across stations of 0.75 mm yr\u003csup\u003e-1\u003c/sup\u003e and a spread from 0.31 mm yr\u003csup\u003e-1\u003c/sup\u003e to 1.19 mm yr\u003csup\u003e-1\u003c/sup\u003e in case 2 (case 1: 0.70 mm yr\u003csup\u003e-1\u003c/sup\u003e [0.31 mm yr\u003csup\u003e-1\u003c/sup\u003e, 1.12 mm yr\u003csup\u003e-1\u003c/sup\u003e]; case 3: 0.80 mm yr\u003csup\u003e-1\u003c/sup\u003e [0.28 mm yr\u003csup\u003e-1\u003c/sup\u003e;1.29 mm yr\u003csup\u003e-1\u003c/sup\u003e]). Natural forcing has also contributed significantly with a median across stations of 0.48 mm yr\u003csup\u003e-1\u003c/sup\u003e and a spatial spread from -0.18 mm yr\u003csup\u003e-1\u003c/sup\u003e to 0.91 mm yr\u003csup\u003e-1\u003c/sup\u003e (case 1: 0.51 mm yr\u003csup\u003e-1\u003c/sup\u003e [-0.21 mm yr\u003csup\u003e-1\u003c/sup\u003e, 1.08 mm yr\u003csup\u003e-1\u003c/sup\u003e]; case 3: 0.45 mm yr\u003csup\u003e-1\u003c/sup\u003e [-0.15 mm yr\u003csup\u003e-1\u003c/sup\u003e;0.75 mm yr\u003csup\u003e-1\u003c/sup\u003e]). In contrast, TWS has contributed, on average, negatively to the observed RSL trends (-0.16 mm yr\u003csup\u003e-1\u003c/sup\u003e [-0.42 mm yr\u003csup\u003e-1\u003c/sup\u003e; 0.02 mm yr\u003csup\u003e-1\u003c/sup\u003e]), which can be explained by\u0026nbsp;the vicinity of many tide gauges to major reservoirs and their associated GRD fingerprints\u003csup\u003e36,37\u003c/sup\u003e. While VLM generally had a smaller average impact than anthropogenic and natural forcings (median of 0.30 mm yr\u003csup\u003e-1\u003c/sup\u003e), its spread is multiple times larger than that of any other contribution with extreme cases ranging from -12.21 mm yr\u003csup\u003e-1\u003c/sup\u003e at Juneau, Alaska to 13.84 mm yr\u003csup\u003e-1\u003c/sup\u003e at Fort Phra Chulachomklao near Bangkok, Thailand. At Juneau, the VLM signal is dominated by uplift related to Glacial Isostatic Adjustment (\u003cstrong\u003eFigure 1a\u003c/strong\u003e)\u003csup\u003e38\u003c/sup\u003e (Larsen et al., 2004), while at Bangkok rapidly accelerated groundwater withdrawals since the 1960s have caused strong nonlinear subsidence (\u003cstrong\u003eFigure 1d\u003c/strong\u003e)\u003csup\u003e26,39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhen assessing the shorter period since 1970, numbers change primarily for the anthropogenic and natural forcings (\u003cstrong\u003eFigure 1h\u003c/strong\u003e). While the anthropogenic contribution increases on average to 0.87 mm yr\u003csup\u003e-1\u003c/sup\u003e [0.13 mm yr\u003csup\u003e-1\u003c/sup\u003e; 1.95 mm yr\u003csup\u003e-1\u003c/sup\u003e], the contribution of natural forcing decreases to 0.30 mm yr\u003csup\u003e-1\u003c/sup\u003e [-0.27 mm yr\u003csup\u003e-1\u003c/sup\u003e; 0.69 mm yr\u003csup\u003e-1\u003c/sup\u003e]. This is generally consistent with the findings for GMSL that also indicate an increasing anthropogenic contribution over the 20\u003csup\u003eth\u003c/sup\u003e century with a particularly strong imprint after 1970\u003csup\u003e19\u003c/sup\u003e. For TWS and VLM, medians across sites remain nearly unchanged, but the spread for VLM increases further (-12.19 mm yr\u003csup\u003e-1\u003c/sup\u003e; 18.19 mm yr\u003csup\u003e-1\u003c/sup\u003e), indicating more extreme VLM cases in the 2\u003csup\u003end\u003c/sup\u003e half of the 20\u003csup\u003eth\u003c/sup\u003e century, likely due to increased fluid withdrawals\u003csup\u003e26\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection and Attribution of ESL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaving disentangled the different forcings of local RSL changes over the 20\u003csup\u003eth\u003c/sup\u003e century, we next extend the attribution framework to ESLs. To do so, we select 124 out of the initial 149 PSMSL records from the Global Extreme Sea Level Analysis v3 (GESLAv3) database\u003csup\u003e24\u003c/sup\u003e covering at least 15 years of hourly tide gauge data. At each location, sea levels are detrended to present-day levels using a one-year moving average. We then estimate return periods and levels using a peak-over-threshold approach, selecting events above the 99.7\u003csup\u003eth\u003c/sup\u003e percentile with a seven-day de-clustering window\u003csup\u003e40,41\u003c/sup\u003e. A Generalized Pareto Distribution (GPD) is fitted to the resulting ESL samples. Long-term changes in return levels are assessed by vertically shifting the return level curve using observed and simulated RSL changes, following a similar approach as used in IPCC AR6 for future ESLs\u003csup\u003e6\u003c/sup\u003e. \u003cstrong\u003eFigure 2\u003c/strong\u003e illustrates this method for Sandy Hook, New Jersey, and Wellington, New Zealand. Sandy Hook’s GPD shape is convex (manifested in a long tail in the distribution), reflecting high ESL variability characterized by a mix of tropical (Hurricanes) and extra-tropical (Nor’easters) storms. A simulated historical 0.44 m RSL rise since 1900 has shifted the 1-in-100-year event to a 1-in-15-year event by 2005 (\u003cstrong\u003eSI Table 2\u003c/strong\u003e), an amplification factor of 6.7 (i.e., an almost sevenfold increase in the likelihood of occurrence; calculated as\u0026nbsp;\u003cimg width=\"52\" height=\"30\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e, where\u0026nbsp;\u003cimg width=\"19\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABMAAAAUCAMAAABYi/ZGAAAAAXNSR0IArs4c6QAAAE5QTFRFAAAAAAAAAAA6ADqQAGa2OgAAOgBmOjqQOma2OpC2OpDbZgAAZjqQZrbbZrb/kDoAkDpmtmYAtmY6tv//25A62////7Zm/9uQ//+2///b9rSCAQAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAcklEQVQoU8VOCRKEIAxrF4/1RFYQ+f9HTSw+wdnMACHTJhF5A7ldYRuUcMkSglI7J9yxMekY/Igndzs4mUjZ9kD2jFCLq9wa/arbOScJcKFd3bQ4aLedIfcIj9jhMRTPGj+Xiq8t8NNvkkM/S1S1Gn/DBeWOBJdVs8tjAAAAAElFTkSuQmCC\" alt=\"image\"\u003e\u0026nbsp;is the amplification factor, and\u0026nbsp;\u003cimg width=\"14\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAUCAMAAACK2/weAAAAAXNSR0IArs4c6QAAAFRQTFRFAAAAAAAAAABmADqQAGa2OgAAOgA6OgBmOjpmOjqQOmaQOma2OpDbZgAAZrb/kDoAkDo6kNv/tmYAttv/tv//25A62////7Zm/9uQ/9u2//+2///bA1Rx0wAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAbklEQVQoU6WP2RLAEAxFo/tiqbRU+f//bDB47vS8kImcGwC/QEZstji8kPAIXko3XwA4lNL0FnzrIoeAHb1IeEGm5ayjk05XN7KdjjhKhEPHiKCyM/rJYhhLkW61YGpa6pq6CwXIoGT77Z3NH3gBmVwEwEMAyvEAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u0026nbsp;and\u0026nbsp;\u003cimg width=\"14\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAUCAMAAACK2/weAAAAAXNSR0IArs4c6QAAAE5QTFRFAAAAAAAAAABmADqQAGa2OgA6OgBmOjpmOmaQOpDbZgAAZrb/kDoAkNv/tmYAtmZmtraQttv/tv//25A627Zm2////9uQ/9u2//+2///bBHo1pgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAZElEQVQoU6WQSw6AIAxEB/+gFhVRuP9FhSK4MybOqi9tZ5oCv0QiaLDZw6kRp5IZj24DqMloagv3dEnCUxUmWE4Fp34tq+3M5R4tgLgKeL0w+un25AAYIVJkwqJXjJeXQ7/94AIV3gOU7XBwYQAAAABJRU5ErkJggg==\" alt=\"image\"\u003e\u0026nbsp;are the return periods in 1900 and 2005, respectively). In contrast, Wellington’s concave GPD shape reflects a short tail in the distribution and thus lower ESL variability. With less than half of the RSL rise at Sandy Hook (i.e., ~0.2 cm), the 1-in-100-year event in 1900 became almost a 2-in-1-year event by 2005 (\u003cstrong\u003eSI Table 2\u003c/strong\u003e), an amplification factor of 181.7. Thus, the shape of the distribution has a strong control over the changes in return periods relative to RSL\u003csup\u003e4\u003c/sup\u003e. Decomposing the ESL return period changes since 1900 into individual forcings shows subsidence as the main driver at Sandy Hook, having reduced the return period to 1-in-30-years, while anthropogenic and natural forcings shifted it in case 2 to 1-in-69-years (anthropogenic, case 1: 1-in-72-years; case 3: 1-in-66-years) and 1-in-75-years (natural, case 1: 1-in-72-years; case 3: 1-in-78-years), respectively. At Wellington, anthropogenic and natural forcings each shortened the return period, in case 2 to 1-in-2-years (anthropogenic, case 1: 1-in-3-years; case 3: 1-in-2-years) and 1-in-6-years (natural, case 1: 1-in-4-years; case 3: 1-in-9-years), respectively, with subsidence contributing only comparatively modestly (1-in-46-years). TWS had a negligible impact on both sites (\u003cstrong\u003eSI Table 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eGlobally, a similar picture emerges (\u003cstrong\u003eFigure 3\u003c/strong\u003e). In all three cases, at 58 out of the 124 analyzed sites the 1-in-100-year event from 1900 increased in likelihood to become at least a 1-in-10-year event in 2005, translating to an amplification factor of 10 or even larger. Only 18 sites (located in Northern Europe, North America, and Japan) experienced a reduction in the frequency of extremes, due to either glacial isostatic adjustment and/or earthquake-related uplift counterbalancing the observed anthropogenic and natural increases in ESLs. On average (median across all 124 sites), the 1-in-100-year event sextupled over the 1900 to 2005 period to a 1-in-16-year event (\u003cstrong\u003eFigure 3b\u003c/strong\u003e). Anthropogenic forcing has been the most important forcing factor behind these changes, on average, tripling the occurrences of the 1-in-100-year event to a 1-in-35-year (case 1: 1-in-35-year; case 3: 1-in-32.5-year) event (\u003cstrong\u003eFigure 3c\u003c/strong\u003e). Natural forcing has also contributed positively, doubling the likelihood of the 1-in-100-year to a 1-in-46.5-year (case 1: 1-in-42-year; case 3: 1-in-48-years) event. VLM has induced the most extreme changes in ESLs locally. For instance, at Manila, Philippines, rapidly accelerated groundwater withdrawals since the 1960s have increased local RSL by ~0.6 m leading to a 600-fold increase in the frequency of a 1-in-100-year event (now occurring more than 3 times a year). However, the median impact across sites has been less than that of anthropogenic and natural forcings; VLM alone has increased the likelihood of the 1-in-100-year event from 1900 to a 1-in-66-year event in 2005. At the same time, TWS has on average increased the ESL return periods slightly (i.e., made them less likely) from a 1-in100 year to a 1-in-104-year event (\u003cstrong\u003eFigure 3c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eImportantly, the forcing factors have been nonstationary over the 20\u003csup\u003eth\u003c/sup\u003e century, leading to varying degrees of influence of RSL change on the associated ESL return periods over time (\u003cstrong\u003eFigure 4\u003c/strong\u003e). While natural and anthropogenic forcings were equally important contributors to observed RSL rates until the mid-1960s with peak natural contributions in the 1930s, anthropogenically driven RSL change has steadily increased at most sites, becoming the most important forcing factor since the mid-1960s (\u003cstrong\u003eFigure 4a\u003c/strong\u003e). Accordingly, most of the naturally forced ESL return period changes occurred before the 1960s, while the anthropogenic forcing became the dominant driver beginning in the late 1970s leading to an accelerated increase in the likelihood of extreme events (i.e., decreasing their return periods) (\u003cstrong\u003eFigure 4b\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion \u0026 Conclusions","content":"\u003cp\u003eOur findings reveal that both global and local processes shape RSL changes and their imprint on ESLs, with anthropogenic forcing now exerting a dominant and growing influence not only on GMSL but also on RSL and the frequency of ESL events. While our analysis isolates RSL-driven trends, we have not explicitly accounted for possible future shifts in storm surge climate\u003csup\u003e10,14\u003c/sup\u003e or tides\u003csup\u003e15,42,43\u003c/sup\u003e. Testing for additional ESL drivers beyond RSL, we compared the trends in the 99.7\u003csup\u003eth\u003c/sup\u003e percentile sea-level threshold, computed over a 15-year moving window, with those in RSL. Significant differences emerged at only three of 124 sites (Wilmington, Esbjerg, Amderma) (\u003cstrong\u003eExtended Data Figure 3g\u003c/strong\u003e), each showing documented long-term tidal range changes\u003csup\u003e15,42,44,45\u003c/sup\u003e without corresponding storm surge trends. This confirms that, at most sites, RSL is the primary driver of ESL changes, consistent with previous studies\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eNevertheless, isolated regional studies have detected storm surge changes of comparable magnitude to individual RSL components in Northern Europe\u003csup\u003e10\u003c/sup\u003e and North America\u003csup\u003e14\u003c/sup\u003e, and while their attribution remains limited by signal-to-noise constraints, such changes can alter ESL return periods on planning-relevant timescales. Refining present-day ESL statistics will require careful accounting for such non-RSL-related non-stationarities and their anthropogenic or natural drivers.\u003c/p\u003e\n\u003cp\u003eThe magnitude of observed return-period changes is also sensitive to the underlying extreme-value methodology and record length\u003csup\u003e41,46,47\u003c/sup\u003e. Distributions computed for sites with shorter records, particularly in low latitudes where tropical-cyclone storm tides are under-sampled\u003csup\u003e48,49\u003c/sup\u003e, can be reshaped by individual record-breaking events, influencing derived non-stationary return periods.\u003c/p\u003e\n\u003cp\u003eDespite these caveats, our analysis fills a key gap in detection and attribution research for coastal ESLs. We show that RSL rise since 1900 significantly shortened ESL frequency: at nearly half the sites, a historical 1-in-100-year event now occurs at least once per decade, and the global median frequency has increased sixfold. Anthropogenic forcing alone has tripled the likelihood of such extremes, with the strongest influence since the mid-1960s, while natural forcings were more influential in the early 20th century. This conclusion holds even under alternative attributions of the underestimated pre-1950 ice-mass loss in CMIP5 models. These results provide robust, observation-based evidence that climate change has already reshaped global coastal flood hazard. The transformation is not a distant prospect, it is underway, underscoring the need for urgent adaptation and sustained mitigation to limit future risk escalation.\u003c/p\u003e\n\u003cp\u003eThese findings also carry implications beyond science and adaptation planning. By directly linking observed increases in ESL frequency to anthropogenic forcing, our results provide a quantitative evidentiary basis relevant to climate litigation and loss and damage claims. Courts increasingly require robust attribution studies to establish causality between greenhouse gas emissions and specific physical impacts\u003csup\u003e50\u003c/sup\u003e. The detection of anthropogenic signals in coastal flood hazard across multiple sites and using observational data strengthens the chain of evidence connecting emissions from identified sources to measurable harm in vulnerable communities. As climate litigation expands globally\u003csup\u003e51\u003c/sup\u003e (Setzer \u0026amp; Higham, 2022), especially in low-lying coastal regions, such attribution analyses may inform both legal arguments and the valuation of damages.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eTide Gauge Data.\u0026nbsp;\u003c/strong\u003eTo assess changes in observed RSL, we select 149 tide gauge records from the PSMSL database\u003csup\u003e23\u003c/sup\u003e, using annual averages from sites that provide at least 50 years of data and are located within 300 km of the nearest CMIP5 model ocean grid point. Consequently, sites in semi-enclosed basins not adequately resolved in CMIP5 models, such as the Mediterranean and Baltic Seas, are excluded. From these 149 PSMSL sites, we further select 124 locations that also provide at least 15 years of hourly records in the GESLA v3 database\u003csup\u003e24\u003c/sup\u003e. Although the attribution analysis is only performed over 1900 to 2005, we consider hourly GESLA records till the end date of the time series in order to maximize the number of extreme events and sites that fulfill the minimum criteria of 15-year data availability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimate Model Data\u003c/strong\u003e. For modelled RSL, we use simulations from CMIP5\u003csup\u003e25\u003c/sup\u003e (\u003cstrong\u003eExtended Data Table 1\u003c/strong\u003e), including both historical and single-forcing experiments (natural and anthropogenic), depending on availability. CMIP6 has not been used here given the lack of glacier model simulations for historical and single-forcing experiments. Although many models offer multiple realizations per forcing experiment, we select only one realization per model to ensure equal weighting across models. Where single-forcing simulations are not available, they are estimated by differencing historical and other single-forcing runs under the assumption of linear forcing response\u003csup\u003e19\u003c/sup\u003e, e.g., \u003cem\u003eAnthropogenic = Historical \u0026ndash; Natural\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eCMIP5-based RSL is composed of three components: sterodynamic sea level, barystatic sea level (from glaciers and ice sheets), and the inverted barometer effect. Sterodynamic contributions are estimated by combining local ocean dynamics (zos) with global mean thermosteric sea level (zostoga). Model drift in both variables is removed using a quadratic fit to pre-industrial control simulations\u003csup\u003e52\u003c/sup\u003e. Following ref. 19, we also apply a 0.1 mm yr⁻\u0026sup1; linear correction to account for ocean disequilibrium prior to 1850, consistent with geological estimates\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGlacier-related barystatic contributions are derived using simulations from ref. 18, which compute mass balance across 19 glacier regions based on local climate conditions. These estimates omit very small and vanished glaciers that are not in the global glacier inventories, and thus we add a global correction of 0.05 mm yr⁻\u0026sup1; \u003csup\u003e54\u003c/sup\u003e, treated as entirely anthropogenic. Glacier mass loss is regionalized using the GRD fingerprints associated with each glacier region\u003csup\u003e35\u003c/sup\u003e. For unlocalized mass loss from missing and vanished glaciers, we apply the 20\u003csup\u003eth\u003c/sup\u003e-century average GRD pattern from ref. 27.\u003c/p\u003e\n\u003cp\u003eIce sheet contributions are parameterized from CMIP5 outputs. For the Greenland ice-sheet surface mass balance (SMB) contribution, the regional climate model MARv3.510 was forced with three CMIP5 models (MIROC5-historical over the period 1900\u0026ndash;2005, NorESM1-historical over the period 1950\u0026ndash;2005 and CanESM1-historical over the period 1950\u0026ndash;2005). Given the large computational requirements of MAR, the output from these simulations is used to establish a statistical relationship between CMIP5 annual snowfall over Greenland, atmospheric summer temperature at 600 hPa and the Greenland surface mass balance\u003csup\u003e55\u003c/sup\u003e, resulting in:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere TT = average June\u0026ndash;July\u0026ndash;August air temperature (CMIP5 variable TA) at 600 hPa (20\u0026ndash;70 W; 60\u0026ndash;85 N) and SF = annual snowfall in mm yr\u003csup\u003e-1\u003c/sup\u003e (CMIP5 variable PRSN, 20\u0026ndash;70 W; 65\u0026ndash;80 N).\u0026nbsp;For Antarctica, surface mass balance is estimated by subtracting evaporation (evspsbl) from precipitation (pr) over the Antarctic, following ref. 19. Annual changes are integrated and converted to global sea-level equivalents, then regionalized using the associated GRD fingerprints.\u003c/p\u003e\n\u003cp\u003eConsistent with prior work\u003csup\u003e19,31\u003c/sup\u003e, both simulated glacier and ice sheet mass loss agree with observations after the 1950s but underestimate earlier trends. This underestimation is attributed to missing internal variability\u003csup\u003e32,33\u003c/sup\u003e and/or overestimated aerosol cooling\u003csup\u003e34,56\u003c/sup\u003e. We follow ref. 19 and ref. 31 and fit a smooth correction function to the pre-1950 mismatch (\u003cstrong\u003eExtended Data Fig. 3\u003c/strong\u003e). As no forcing breakdown is possible for this mismatch, we assume three different test cases, in which the fraction of natural and anthropogenic forcings are varied for the correction function (case 1: 100% natural, case 2: 50% natural and 50% anthropogenic, case 3: 100% anthropogenic). These corrections are regionalized using observed average GRD fingerprints from ref. 27.\u003c/p\u003e\n\u003cp\u003eInverted barometer effects are estimated from CMIP5 sea-level pressure (psl) using the hydrostatic relationship\u003csup\u003e57\u003c/sup\u003e, assuming a 1 cm sea-level rise per 1 hPa drop in local pressure. While globally neutral, this effect can significantly alter local RSL\u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAll CMIP5 outputs are sampled at the nearest neighboring grid point to a tide gauge and smoothed to reflect climate-related variability at timescales longer than 30 years using Singular Spectrum Analysis with an embedding dimension of 15 years\u003csup\u003e59\u003c/sup\u003e. Model ensemble statistics are provided as medians across the 10 models, with inter-model spread given by the standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTWS Contribution\u003c/strong\u003e. TWS changes, due to dam impoundment\u003csup\u003e28\u003c/sup\u003e, groundwater withdrawal\u003csup\u003e29\u003c/sup\u003e, and hydrological variability\u003csup\u003e30\u003c/sup\u003e, are not included in CMIP5. We therefore adopt observational TWS estimates from the compilation in ref. 27, using a 100-member ensemble to sample the associated uncertainties over time. No forcing breakdown is applied for this term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVLM Contribution\u003c/strong\u003e. VLM is presently inferred primarily from the Global Navigation Satellite System (GNSS) or Interferometric Synthetic Aperture Radar (InSAR) data, although these are limited in spatial and temporal coverage\u003csup\u003e60,61\u003c/sup\u003e. To cover the entire 20\u003csup\u003eth\u003c/sup\u003e century and include nonlinearities in VLM, we use estimates from ref. 26, in which VLM is calculated as the residuals between local tide gauges and nearby sea-level reconstructions\u003csup\u003e22\u003c/sup\u003e, resolving decadal and nonlinear signals with ~1 mm yr⁻\u0026sup1; accuracy. At 16 sites (Aburatsubo, Wajima, Hosojima, Onahama, Tonoura, Mera, Manila, Legaspi, Ko-Lak, Fort Phra Chulachomklao, Galveston, Prince Rupert, Port Isabel, Grand Isle, Lyttleton, and Yakutat), we consider nonlinear VLM effects primarily related to time-varying fluid withdrawals and/or earthquakes and volcanic activity. At those sites a Gaussian Process is used to filter out decadal and longer signals of VLM. For sites at which tide gauge recordings start later in the 20\u003csup\u003eth\u003c/sup\u003e century, we assume (depending on the site) that the nonlinear VLM converges to either the local GIA signal (e.g., at Fort Phra Chulachomklao near Bangkok, \u003cstrong\u003eFigure 1d\u003c/strong\u003e) or to the associated local linear VLM rate before observations become available. At the other 133 locations a linear least squares fit is estimated over the entire observational period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtreme Value Statistics\u003c/strong\u003e. We follow the best practices described in ref. 40 and applied in most recent IPCC reports\u003csup\u003e2,6\u003c/sup\u003e to select and model ESLs from observations. First, the hourly sea-level records are detrended to present-day (i.e., adjusting historical values to reflect present-day conditions without a trend) using a moving one-year average filter. Then a peak-over-threshold approach is applied to select ESLs that are larger than the 99.7\u003csup\u003eth\u003c/sup\u003e percentile of the entire detrended hourly record. The detected events are de-clustered using a de-clustering timescale of seven days that ensures that only independent events enter the ESL sample. A GPD distribution is then fitted to the resulting sample of threshold exceedances to estimate associated return periods and levels. To test non-stationarity in different GPD parameters, the same analysis is also performed, where possible, using a 15-year running window. We note that systematic long-term changes in distribution parameters were primarily detected for the location parameter (i.e., the threshold, the 99.7\u003csup\u003eth\u003c/sup\u003e percentile), while changes in shape and scale are limited to a few isolated sites (\u003cstrong\u003eExtended Data Fig. 4\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e. The tide gauge data used in this study is publicly available from the Permanent Service of Mean Sea Level (https://www.psmsl.org/), while the GRD fingerprints and VLM estimates at individual locations are accessible from the ref. 27. All data required to perform the analysis is available via the ZENODO repository 10.5281/zenodo.16995841.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e. All code required to perform the analysis is available via the ZENODO repository 10.5281/zenodo.16995841.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eS.D., P.M., and T.W. acknowledge the National Science Foundation grant ICER-2103754 (as part of the Megalopolitan Coastal Transformation Hub). S.D. acknowledges NASA grant 80NSSC24K1529 and David and Jane Flowerree for their endowment funds. T.W. acknowledges NASA grant 80NSSC24K1527.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e: Correspondence and requests should for materials should be addressed to S.D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eS.D. designed and performed the research and wrote the first draft of the paper. Q.S. developed the CMIP5 sea-level database and handled the GESLA dataset. P.M. and T.W. supported the coding of the GPD analysis. A.B.A.S. and B.M. provided the Greenland ice sheet and glacier data for CMIP5 models. All authors shared ideas and contributed to the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNicholls, R. J. (2018). Adapting to sea-level rise. \u003cem\u003eResilience\u003c/em\u003e, 13-29.\u003c/li\u003e\n \u003cli\u003eOppenheimer, M., Glavovic, B., Hinkel, J., van de Wal, R., Magnan, A. K., Abd-Elgawad, A., ... \u0026amp; Sebesvari, Z. (2019). Sea level rise and implications for low lying islands, coasts and communities.\u003c/li\u003e\n \u003cli\u003eCooley, S., D. Schoeman, L. Bopp, P. Boyd, S. Donner, D.Y. Ghebrehiwet, S.-I. Ito, W. Kiessling, P. Martinetto, E. Ojea, M.-F. Racault, B. Rost, and M. Skern-Mauritzen, 2022: Oceans and Coastal Ecosystems and Their Services. 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Measuring, modelling and projecting coastal land subsidence. \u003cem\u003eNature Reviews Earth \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 40-58.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7491013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7491013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal flooding events are escalating worldwide, yet the role of human-driven sea-level rise remains poorly constrained. Here we provide the first global detection and attribution of changes in extreme sea-level (ESL) frequency since 1900, combining tide gauge records with historical and single-forcing experiments from the Coupled Model Intercomparison Project (CMIP5). We show that relative sea-level (RSL) rise, driven primarily by anthropogenic forcing since the 1960s, has already transformed the likelihood of historically rare extremes. Globally, the median frequency of a historical 1-in-100-year ESL event has increased sixfold, with human-driven forcing alone tripling the likelihood of such events. Natural variability still modulates regional patterns but has become secondary along most coastlines. These findings provide direct, observation-based evidence that climate change has already reshaped coastal flood hazard, underscoring the urgency of integrating attribution science into coastal adaptation, risk management, and policy frameworks.\u003c/p\u003e","manuscriptTitle":"Human-driven sea-level rise has tripled the frequency of coastal sea-level extremes since 1900","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 10:22:24","doi":"10.21203/rs.3.rs-7491013/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-climate-change","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nclimate","sideBox":"Learn more about [Nature Climate Change](http://www.nature.com/nclimate/)","snPcode":"","submissionUrl":"","title":"Nature Climate Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bff30e8a-6634-476c-a565-1f042600cc8a","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54158049,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts"},{"id":54158050,"name":"Earth and environmental sciences/Climate sciences/Ocean sciences/Physical oceanography"},{"id":54158051,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-11-07T10:22:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 10:22:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7491013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7491013","identity":"rs-7491013","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}