# River flow amplification under climate change: attribution and climate-driven storylines of the winter 2023/24 UK floods

Wilson Chan, Lucy J Barker, Davide Faranda, Jamie Hannaford

► To cite this version:

Wilson Chan, Lucy J Barker, Davide Faranda, Jamie Hannaford. River flow amplification under climate change: attribution and climate-driven storylines of the winter 2023/24 UK floods. Environmental Research Letters, In press, 10.1088/1748-9326/adfdfe. hal-05230897v2

HAL Id: hal-05230897

https://hal.science/hal-05230897v2

Submitted on 10 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
<figure><img src="image_2.png" /></figure>

LETTER • OPEN ACCESS

River flow amplification under climate change: attribution and climate-driven storylines of the winter 2023/24 UK floods

To cite this article: Wilson C H Chan et al 2025 *Environ. Res. Lett.* 20 104035

View the [article online](#) for updates and enhancements.

You may also like

- Climatological analysis of tropical cyclone impacts on hydrological extremes in the Mid-Atlantic region of the United States
Ning Sun, Mark S Wigmosta, David Judi et al.

- Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study
T I E Veldkamp, F Zhao, P J Ward et al.

- Global implications of 1.5 °C and 2 °C warmer worlds on extreme river flows
Homero Paltan, Myles Allen, Karsten Haustein et al.

UNITED THROUGH SCIENCE & TECHNOLOGY

<figure><img src="image_3.png" /></figure>

The Electrochemical Society
Advancing solid state & electrochemical science & technology

248th
ECS Meeting
Chicago, IL
October 12-16, 2025
Hilton Chicago

<figure><img src="image_4.png" /></figure>

Science +
Technology +
YOU!

Register by
September 22
to save $$

REGISTER NOW
<figure><img src="image_5.png" /></figure>

CrossMark

OPEN ACCESS

RECEIVED

7 March 2025

REVISED

11 July 2025

ACCEPTED FOR PUBLICATION

21 August 2025

PUBLISHED

9 September 2025

Original content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.

Any further distribution
of this work must
maintain attribution to
the author(s) and the title
of the work, journal
citation and DOI.

<figure><img src="image_6.png" /></figure>

LETTER

# River flow amplification under climate change: attribution and climate-driven storylines of the winter 2023/24 UK floods

Wilson C H Chan<fnref n="1" />,*, Lucy J Barker<fnref n="1" />, Davide Faranda<fnref n="2" />,<fnref n="3" />,<fnref n="4" /> and Jamie Hannaford<fnref n="1" />,<fnref n="5" />

E-mail: wilcha@ceh.ac.uk

**Keywords:** flooding, storylines, attribution, hydrological extremes, river flows

Supplementary material for this article is available [online](#)

## Abstract

Climate change is expected to alter the magnitude and frequency of river floods. Post-event analyses often assess the rarity of events from historical observations, and a growing number of studies attempt to attribute the event's severity to anthropogenic warming. Recent studies also advocate for the creation of 'what if' event storylines to explore the consequences if an observed event turned out to be worse. However, few studies have harmonized these different approaches when conducting retrospective analyses of hydrological extremes. Climate change attribution including river flows also remains rare. Here, a framework for post-event analyses of hydrological extremes is demonstrated using the winter half-year 2023/24 UK river flooding as a case study. Persistent high river flows were observed across the UK and western Europe, following on from notable winter floods in 2013/14, 2015/16 and 2019/20. The 'ClimaMeter' analogue-based attribution suggests that a 6 month period with similar atmospheric circulation patterns to the observed winter half-year 2023/24 has become warmer and wetter (by an average 8.8%) in 1945-2021 compared with 1850-1925. Monthly river flow reconstructions extended back to 1850 show that river flows during the analogue periods in the recent past have also become 13.5% higher. Applying the UNSEEN approach by pooling seasonal hindcasts show the potential for river flows to be 46% higher than the baseline in a worst-case storyline. Finally, hydrological simulations driven by a single-model initial-condition large ensemble suggest that when accounting for internal variability, a robust climate signal in winter half-year river flows has emerged for some areas but may remain concealed until the mid-21st century and beyond. Our results contribute to the use of storyline approaches in post-event analysis and highlight the changing risk of winter flooding in the UK. This framework can be applied to future hydrological extremes both in the UK and elsewhere to inform long-term planning for climate adaptation.

## 1. Introduction

A warming climate is expected to alter the timing, magnitude, frequency and spatial pattern of floods (Arnell and Gosling 2016). Studies have also found an increase in widespread, spatially extensive floods in observations (e.g. Fang et al 2024) and future projections (Griffin et al 2024). The thermodynamic response of a warming climate is well understood, as

a warmer atmosphere holds more moisture follow-
ing the Clausius–Clapeyron relationship. However,
understanding the changing risk of these extremes
is challenging under non-stationarity. In a sign of
increasing hydrological volatility, floods also increas-
ingly follow on or occur in conjunction with other
weather and climate extremes, such as tempor-
ally compounding events resulting from windstorms
clusters (Bevacqua et al 2020).

<footnote n="1">¹ UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire OX10 8BB, United Kingdom</footnote>

<footnote n="2">² Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, CE Saclay l'Orme des Merisiers, 91191 Gif-sur-Yvette, France</footnote>

<footnote n="3">³ London Mathematical Laboratory, 8 Margravine Gardens, London W6 8RH, United Kingdom</footnote>

<footnote n="4">⁴ LMD/IPSL, ENS, Université PSL, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université, CNRS, Paris, France</footnote>

<footnote n="5">⁵ Irish Climate Analysis and Research UnitS (ICARUS), Maynooth University, Maynooth, Co. Kildare, Ireland</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
Researchers often assess the statistical rarity of notable events and whether the event is part of a trend, such as analysis following successive UK river flooding events across the winters of 2013/14 (Muchan et al 2015), 2015/16 (Barker et al 2016) and 2019–21 (Sefton et al 2021, Griffin et al 2025). Methodologies to detect river flow trends are widely used, and some studies have suggested that the period since the 1990s has been notably flood-rich across western and central Europe (Blöschl et al 2020). However, river flows are highly variable, and most records are short (e.g. many UK gauging stations were installed in the 1960s/70s) (Dixon et al 2013). Identifying emerging trends from short observations is challenging, as historical or near-term trends may oppose the long-term climate-driven trend given large climate variability (Wilby 2006, Deser and Phillips 2023, Chan et al 2025). A growing number of studies are also attempting to attribute the effects of anthropogenic warming by comparing the probability of an event class in climate model simulations of the present-day climate and a climate without anthropogenic warming (Stott et al 2016) (e.g. World Weather Attribution: www.worldweatherattribution.org/). Attribution including river flows remains challenging as hydrological responses do not scale linearly with rainfall (Scussolini et al 2024) and coarse climate model resolutions preclude detailed catchment simulations. Past probabilistic flood attribution has focused on short-duration river flow flood peaks (e.g. Gillett et al 2022). In the UK, attribution for the autumn 2000 and winter 2014/15 floods using a hydrological model were uncertain, and results ranged from no attributable risk to increased risk with climate change (Kay et al 2011, 2018). This reflects the challenges associated with attributing events driven by strong atmospheric circulation anomalies and the uncertain response of atmospheric circulation to climate change (Shepherd 2014).

A storyline approach has recently emerged for climate change attribution and exploring plausible worst-cases (Shepherd et al 2018, Sillmann et al 2021). For example, the ClimaMeter attribution approach (www.climameter.org/; Faranda et al 2024) samples for events with similar atmospheric circulation patterns to the target event in historical observations or reconstructions. Unlike probabilistic attribution, ClimaMeter anchors attribution to the specificity of the target event, rather than aggregating across events with different dynamical drivers. It has been applied for heatwaves (Jézéquel et al 2018), heavy rainfall (Thompson et al 2024) and meteorological droughts (Faranda et al 2023b) and has been recently extended beyond these more traditional variables to storm surges by coupling with impact models (Faranda et al 2023a). The storyline approach also encourages more routine creation of ‘what if’ downward counterfactuals to enhance risk awareness (i.e. what if an observed event turned out worse?) (Ommer et al 2024, Kelder

et al 2025). Worst-case storylines can be created by perturbing the event's drivers to explore alternative versions of the event in space, time or magnitude (Lin et al 2020, Chan et al 2022, Heinrich et al 2024). Unprecedented outcomes can also be sampled from initialized climate model simulations, following the unprecedented simulations of extremes using the UNprecedented Simulations of Extremes using ENsembles (UNSEEN) technique (Thompson et al 2017, 2025). Kay et al (2024) used initialized large ensemble simulations with a national UK hydrological model to show that, in the worst-case storyline, river flows during the flooding in autumn and winter 2023/24 could have been 42% higher.

Traditional standalone event reports and attribution studies are often separate pieces of analysis and seldom cover the same domain or time horizon. Few studies to date have attempted to harmonize the different approaches when conducting retrospective analysis of hydrological extremes. Here, we take the winter half-year 2023/24 UK river flooding as a case study to demonstrate a framework for analysing hydrological extremes that combines event attribution and ‘what if’ counterfactual storylines and appraises trend detectability in the context of internal climate variability. The specific aims are to:

* place the 2023/24 UK river flooding in the context of past climate change for both rainfall and river flows following the ‘ClimaMeter’ attribution approach;

* quantify the present-day risk of high rainfall using the UNSEEN approach and explore worst case storylines of the 2023/24 river flooding;

* investigate the effects of internal variability on winter half-year river flows using a single-model initial-condition large ensemble and estimate when changes are statistically detectable beyond historical variability (i.e. time of emergence (ToE)).

## 1.1. Winter half-year 2023/24

The winter half-year October 2023–March 2024 was exceptionally wet across the UK (figure S1). Central, eastern England and northeast Scotland saw their highest total winter half-year rainfall since 1836 (figure 1(a)) and winter 2024 was one of the wettest on record. Consequently, many catchments registered their highest average winter half-year and winter (December–Feburary) river flows (figure 1(b)) (Hannaford et al 2024). Flood impacts were widespread, with transportation disruptions, property flooding and loss of agricultural crops. A clustering of storms brought exceptional rainfall accumulations, with 12 named storms in the 2023/24 storm season (nine within the winter half-year), the highest since the naming system began in 2015 (Met Office 2024). Clarke et al (2024) found that extreme rainfall due to Storm Babet in October 2023 has become more likely and intense across Ireland with climate change.
<figure><img src="image_7.png" /></figure>

Storm Ciarán in November 2023 was comparable to the Great Storm of 1987, but impacts in the UK were muted as it followed a southerly track with the strongest impacts felt across northern France (Winter et al 2024). Attribution of the 2023/24 storm season showed that rainfall on stormy days has increased by 30% relative to pre-industrial times, with a 6%–25% increase in winter half-year rainfall (Kew et al 2024).

## 2. Methods and data

Figure 2 shows a schematic of the various observational and modelled data, methodological steps and outputs of this study. The methods are described further below.

### 2.1. Circulation analogue attribution

The ClimaMeter approach aims to place the high rainfall and river flows over the winter half-year 2023/24 in the context of historical climate change. Atmospheric circulation analogues with similar mean sea level pressure (MSLP) patterns to the observed winter half-year 2023/24 are sampled from the recent past (factual: 1945–2021) and a more distant past with less anthropogenic warming (counterfactual: 1850–1925). MSLP data over 1850–2021 are obtained by combining the NOAA Twentieth Century (20CRv3) (1850–2015) and the NCEP/DOE (2016–2021) reanalysis for the Euro-North Atlantic region

(70° W–30° E, 20°–80° N) (further details in supplementary section S1). A 6 month backwards rolling average is applied to MSLP (i.e. SLP-6) with the observed winter half-year 2023/24 event described by SLP-6 ending March 2024. The SLP-6 analogues most similar to the winter half-year 2023/24 are sampled from the factual and counterfactual periods. Similarity is defined by the top 30 SLP-6 maps with the lowest Euclidean distance compared with SLP-6 of the observed winter half-year 2023/24. This follows established methods for selecting circulation analogues, as outlined in Faranda et al (2024) and Jézéquel et al (2018). The effect of climate change is estimated by comparing the rainfall and temperature between analogues in the counterfactual and factual periods.

Monthly river flow reconstructions for 200 UK catchments (figure S2) for the 1850–2021 period are conducted to consider possible river flows during the analogue periods. Attribution of the persistent high river flows over a 6 month period is achieved by comparing the average river flow anomalies during analogue periods between the counterfactual and factual periods. Previous UK river flow reconstructions cover 1891–2015 (Smith et al 2018) but not the full 1850–2021 period. High-resolution gridded monthly meteorological observations from the HadUK-Grid dataset for the mid-19th century have been improved after data rescue and
<figure><img src="image_8.png" /><figcaption>**Figure 2.** Schematic diagram of the datasets and methodology involved in this study.</figcaption></figure>

digitization (Hawkins *et al* 2023). Correspondingly, we created monthly river flow simulations using the GR2M hydrological model, driven by 1 km HadUK-Grid rainfall (1850–2021) and monthly potential evapotranspiration (calculated using the McGuiness-Bordne temperature-based approach using gridded temperature from the EUSTACE dataset from 1850–1883 and 1 km HadUK-Grid temperature from 1884–2021). The calibration of the GR2M model follows that of O’Connor *et al* (2021) and is further described in section S2.1. Reconstructions for 51 Irish catchments using GR2M from O’Connor *et al* (2021) are included for a comprehensive view across the British and Irish Isles. GR2M performs well with observations across most catchments (figure S3, section S3) and reconstructed flows correspond well with existing reconstructions over the period 1891–2015 (figure S4), revealing flood-rich periods prior to the instrumental record. The 6 month analogue selection window considers river flow response time and maximizes the sample size available for analogue selection. However, antecedent conditions for each analogue are different, possibly underestimating changes for slow-responding (e.g. groundwater-dominated) catchments. Thus, results are compared with an

additional sensitivity test where monthly GR2M simulations are initialized with observed data up until September 2023, then run forwards with rainfall and PET from each 6 month analogue period, keeping antecedent conditions constant.

## 2.2. Alternative storylines for winter half-year 2023/24

It is valuable for risk awareness and adaptation to imagine how events could have turned out worse. The potential for even higher rainfall and river flows over winter 2023/24 is examined by sampling for extremes within initialized simulations. The SEAS5 seasonal hindcast (Johnson *et al* 2019) provides 2850 simulated winters over the period 1982–2020 (i.e. pooled simulations initialized in September, October and November; 38 years × 25 ensemble members × 3 lead times) (further described in section S3.1). A simple bias adjustment factor was applied to match the monthly mean observed rainfall and temperature. Statistical tests in Thompson *et al* (2017) are applied to compare the statistical moments of simulated rainfall with observations to ensure that simulated rainfall is statistically indistinguishable from observations (section S3.2). The presence of ensemble
member dependence and model drift between lead times could reduce the effective sample size (Kelder et al 2022). The credibility of average UK winter rainfall from SEAS5 was confirmed in Chan et al (2024), showing that ensemble members can be treated as independent and that there is no evidence of model drift in the hindcast ensemble.

The large sample of winter weather sequences from SEAS5 is used to drive the GR6J hydrological model to explore plausible storylines over winter 2023/24 (section S2.2). GR6J is a widely used daily hydrological model for both forecasting and climate change projections and was calibrated for the same 200 catchments as in section 2.1, showing satisfactory model performance over the winter half-year 2023/24 (figures S5 and S6). For exploring alternative storylines of the winter 2023/24, GR6J is used as a daily time-step enabling more detailed simulation of temporal flood dynamics. Simulations also benefit from the availability of daily meteorological observations from the HadUK-Grid 1 km dataset over the winter half-year 2023/24. All 2850 bias-adjusted hindcast weather sequences are treated as individual realizations of plausible winters, following that of Chan et al (2024) and Kay et al (2024). For each catchment, GR6J is initialized from 1961 until November 2023, accounting for wet antecedent catchment conditions in autumn 2023, then run forwards using each SEAS5 winter. Hydrological model parameter uncertainty can be large when simulating individual events (Brigode et al 2013). To estimate this, the top 20 parameter sets from calibration are used to simulate river flows from each of the 2850 SEAS5 winters.

## 2.3. ToE

Single-model initial-condition large ensembles (SMILEs) can be used to estimate the timing when changes are estimated to exceed the range of internal variability (i.e. ToE) as they explicitly quantify internal variability. The CRCM5-LE large ensemble, a 50-member SMILE dynamically downscaled over Europe (Leduc et al 2019), is used to drive the GR6J hydrological model to estimate ToE for winter half-year river flows into the 21st century. Details of CRCM5-LE can be found in section S3.3. Simulated river flows by GR6J, with the same calibration strategy as previously noted, are taken from Chan et al (2025), which applied the ToE framework for low flows in Great Britain (GB). The ToE for each catchment is obtained following the approach in Faghih and Brissette (2023). For each catchment, average winter half-year river flows over the period 1981–2010 are calculated for each ensemble member. The range of internal variability is defined as ±1 standard deviation of the average flow across all 50 ensemble members. The average for each ensemble member is then computed over successive overlapping 30 year periods (e.g. 1982–2011, 1983–2012, ... 2068–2098). The ToE is defined as the middle year of the 30 year

period where the ensemble mean difference in winter half-year flows relative to the baseline period exceeds internal variability.

# 3. Results

## 3.1. Circulation analogue attribution

The top 30 SLP-6 analogues for both counterfactual (1850–1925) and factual (1945–2021) periods exhibit low pressure centred over the British Isles and high pressure over Greenland (figures 3(a)–(c)), a pattern that is expected to bring cyclonic conditions and higher UK rainfall (e.g. Richardson et al 2018). The analogues also show a deepening of the low pressure in the factual period (figure 3(d)). Analogue quality, the average Euclidean distance of the circulation pattern of the observed event from its closest 30 analogues, shows that the observed 2023/24 circulation pattern was relatively unusual, although analogues in the factual period are slightly more similar to the observed event (figure S7(a)). Analogues in both periods are generally found in winter half-year months (figure S7(b)).

Analogues are associated with positive rainfall anomalies, particularly for England and northeast Scotland (figures 3(e)–(h)), consistent with the spatial pattern of the observed event. Temperatures have warmed by >1 °C, with the greatest warming across southeast England (figures 3(i)–(l)). UK rainfall averaged across the analogues is 8% higher in the factual period, with spatial variations (e.g. 14% higher for the East Midlands) (table S3). This increase in rainfall exceeds the rate of the Clausius–Clapeyron relationship, suggesting dynamical and atmospheric circulation influences. The dependence of analogues on modes of natural variability suggests that El Niño may have played a larger role for analogues in the factual period (figure S8(c)), but no such dependence on the Atlantic multidecadal variability was found (figure S8(d)). An El Niño phase is more likely to be associated with cyclonic days and wet UK winters (Fraedrich and Müller 1992).

River flows during analogue events in both factual and counterfactual periods match the spatial pattern of observed river flow anomalies over winter half-year 2023/24 (figures 3(m)–(o)). However, observed anomalies are much greater than the analogue mean, highlighting the event's severity. River flow anomalies are on average 13.5% higher in the factual period (figure 3(p)), with the greatest increase for catchments in the East Midlands (+26%), followed by Yorkshire and Humber (+22%) (table S4). For the Republic of Ireland, events similar to winter half-year 2023/24 had 9.9% higher flows in the factual period. Larger changes in river flows compared with rainfall highlight the non-linearity in catchments' response to rainfall, which can exceed changes in rainfall of a given magnitude. Keeping the antecedent conditions prior to October 2023 constant yields very similar
<figure><img src="image_9.png" /></figure>

results, with river flows on average +12.5% higher than in the counterfactual period (regional statistics in table S5). For the post-1891 period, existing river flow reconstructions provide an opportunity to test the sensitivity of the results to different periods and hydrological models. The choice of an alternative counterfactual (1891–1940) and factual (1966–2015) period and simulations by different models show consistent changes with higher flow anomalies in the factual period but differences in river flows are slightly higher (e.g. for Scotland) (figure S8 and table S6).

In summary, a 6 month period with a similar atmospheric circulation pattern to the winter half-year 2023/24 has become wetter and warmer with higher river flows, attributable to anthropogenic climate change since 1850 and potentially exacerbated by natural variability associated with El Niño.

## 3.2. Potential for unprecedented extremes

In this section, the potential for more extreme rainfall and river flows is examined by following the UNSEEN approach using the SEAS5 hindcast archive.

The robustness and credibility of the SEAS5 simulated rainfall assessed via model fidelity tests shows that bias-adjusted simulated winter rainfall in SEAS5 can be considered statistically indistinguishable from observations for large parts of the UK in December and January (figure S9). However, almost all regions fail the test in February, with the variability in the modelled rainfall being lower than the observations. This was also found in Kelder et al (2022) and Kay et al (2024), the latter with a different set of initialized hindcasts. The present-day chance of exceeding observed rainfall for winter 2023/24 is shown in figures 4(a)–(c). On average, there is an 18%, 52% and 21% chance of exceeding the observed December, January and February rainfall, respectively, with significant regional variations (table S7). Observed February 2024 rainfall is particularly hard to beat with a very low chance of exceedance across England and considerable uncertainty in the magnitude of unprecedented rainfall (e.g. for the East of England; figures 4(d)–(f)). February 2024 was the wettest since 1836 for several regions, hence a low chance of exceedance, but estimates could
<figure><img src="image_10.png" /></figure>

be underestimated as simulated February rainfall in SEAS5 exhibits lower variability compared with observations.

Daily river flow simulations initialized in November 2023 with all 2850 SEAS5 winters show the potential for significantly higher river flows than observed, including higher *n*-day maximum river flows (defined as the highest average flows over a period of 14, 30 and 60 days across 2023/4). In the worst case, *n*-day maxima could be at least 1.5 times higher than the baseline winter 2023/24 event (figure 5(a)). The proportion of storyline simulations breaking the observed 14 day maximum flows is higher compared with exceeding the observed average winter half-year river flows, especially across catchments in Wales and southern England (figure 5(b); table 1 for all regions). Averaged across all regions, 7.2%–8.3% of all winters in the SEAS5 hindcasts result in higher average winter flows relative to the baseline. Autumn 2023 was wet across the UK, with parts of the Midlands receiving over 1.5 times the average rainfall (Met Office 2023). The combination of wet antecedent conditions at the end of November in conjunction with the wettest SEAS5 winter suggests winter flows could be 41.3%–48.9% higher than the already exceptionally high flows in the baseline.

### 3.3. ToE

The timing of a robust climate-driven signal (ToE) may not be statistically detected given short observational records and the influence of internal

climate variability superimposed on a climate-driven trend. In this section, the ToE for winter half-year mean river flows is estimated using the 50-member CRCM5-LE SMLE. Figure 6(a) shows, for six regions in GB, the difference in winter half-year river flows in successive 30 year periods from the average winter half-year river flows over the baseline period. Winter half-year river flows are projected to increase with climate change for all regions, but the timing of when the ensemble mean exceeds estimated internal variability varies between regions. Catchments in some parts of northern Scotland have an early ToE where a robust climate change signal of increasing winter half-year flows has already emerged or may emerge imminently (e.g. within the next decade) (figure 6(b)). However, there are several regions (e.g. North West England and Wales) where the estimated ToE for some catchments is towards the late 21st century, suggesting that a robust climate change signal is not estimated to emerge for several decades. This could relate to the large variability (i.e. noise) in flows across the ensemble members in these flashy catchments but could also be related to climate-model-related uncertainty. Chan et al. (2025) found earlier ToE, within the next two decades, across some GB catchments for high flows (5th percentile of the flow duration curve—Q5), suggesting that uncertainty in rainfall changes for the shoulder months (i.e. October, November and March) due to internal variability could conceal a robust climate-driven trend.
<figure><img src="image_11.png" /><figcaption>**Figure 5.** (a) Maximum deviation of *n*-day (14, 30 and 60 day) maximum river flow in the worst-case storyline expressed as a ratio relative to the baseline winter half-year 2023/24 event for all UK regions as shown by the horizontal red dashed line (abbreviations of UK regions are shown in figure S2 in the supplement). Proportion of SEAS5 storyline simulations exceeding the baseline for (b) 14 day maximum river flow and (c) average winter half-year 2023/24 river flows across England and Wales. England and Wales is visualized here as river flooding from the observed event was most severe for these constituent countries of the UK.</figcaption></figure>

**4. Discussion and conclusion**

This study demonstrates a framework for post-event analyses of hydrological extremes that combines event attribution, ‘what if’ storylines and the use of large ensemble simulations to appraise climate-driven trend detectability. Taking the winter half-year 2023/24 UK river flooding as a case study, ClimaMeter attribution shows that events with similar atmospheric circulation patterns have become wetter (by an average 8.8%) since 1850, consistent with probabilistic attribution of the winter 2023/24 storm season (Kew et al 2024). UK winters in the last decade were 9% (24%) wetter than 1991–2010 (1961–1990) (Kendon et al 2024). Monthly river flow reconstructions extended back to 1850 show that average river flows during analogue events are higher in the factual period (1945–2021) by 13.5%,

highlighting the non-linearity of the response of river flow to rainfall. The 6-months analogue selection period is designed to capture longer-term extremes, as the main driver of UK river flooding is saturation excess, where high flows occur after a series of rainfall events, rather than a singular largest rainfall event or being driven by snowmelt (Berghuijs et al 2016, Kay 2016). However, a limitation of the ClimaMeter approach is the reliance on historical observations/reconstructions so the years available for analogue selection are limited. Analogue quality for the winter half-year 2023/24 suffers given limited sample size, especially if circulation patterns of the observed event are rare. Spectral nudging simulations (e.g. Athanase et al 2024) or sampling for analogues within large climate model simulations in pre-industrial, present-day and future climates could complement these results. Additionally, our study did
<table><caption>**Table 1.** Percentage of simulations exceeding the average winter river flows in the baseline (simulated river flows over the observed winter 2022/23) and the maximum change in river flows (%) relative to the baseline averaged across all catchments in each region. The uncertainty range represents hydrological model parameter uncertainty (i.e. the maximum and minimum value across the top 20 parameter sets).</caption><thead><tr><th>Region</th><th>Percentage of simulations exceeding baseline</th><th>Maximum change in mean river flows relative to baseline (%)</th></tr></thead><tbody><tr><td>North Scotland</td><td>15.3 (14.3–16.1)</td><td>43.5 (41.1–45.5)</td></tr><tr><td>East Scotland</td><td>13.1 (12.6–13.8)</td><td>39.8 (36.9–42.6)</td></tr><tr><td>West Scotland</td><td>21.2 (20.4–21.7)</td><td>59.5 (55.7–63.1)</td></tr><tr><td>North East England</td><td>6.5 (6.2–7)</td><td>41.0 (36.5–44.3)</td></tr><tr><td>North West England</td><td>3.0 (2.8–3.3)</td><td>41.7 (38.4–44.2)</td></tr><tr><td>Yorkshire and Humber</td><td>2.0 (1.9–2.2)</td><td>32.1 (30–34.3)</td></tr><tr><td>East Midlands</td><td>1.7 (1.5–1.9)</td><td>26.3 (24–28.3)</td></tr><tr><td>West Midlands</td><td>5.9 (5.6–6.3)</td><td>57.7 (52.6–61.6)</td></tr><tr><td>Wales</td><td>5.9 (5.6–6.5)</td><td>49.2 (46–51.3)</td></tr><tr><td>East of England</td><td>4.4 (3.6–5.1)</td><td>48.1 (40.1–57.6)</td></tr><tr><td>South East England</td><td>7.8 (6.9–8.8)</td><td>59.1 (52.2–66.1)</td></tr><tr><td>South West England</td><td>6.1 (5.4–6.8)</td><td>45.4 (41.6–48.5)</td></tr></tbody></table>

<figure><img src="image_12.png" /></figure>

not attribute changes to individual flood peaks as river flow reconstructions at finer temporal resolution would be required. Uncertainty in the quality of the rainfall observations in the early 19th century, such as comparatively poor gauge density despite data rescue efforts, a lack of standardization and biases related to under-catch of snowfall (Murphy et al. 2020), may further affect attribution results.

Alternative storylines of winter 2023/24 showed the potential for river flows to be ~46% higher in the worst case, reflecting the compound occurrence of extreme winter rainfall with already high observed antecedent wetness in autumn 2023. This is consistent with findings in Kay et al. (2024) using

a different hindcast dataset and hydrological model. The creation of event storylines contributes to calls for routine exploration of how observed events could have turned out worse (Woo 2019, Sillmann et al. 2021), and adds to the use of the UNSEEN technique to constrain the present-day potential of hydrological extremes (e.g. Brunner and Slater 2022). Additional storylines, such as shifting rainfall fields to maximize rainfall accumulation in certain locations (e.g. Goulart et al. 2024, Merz et al. 2024), ensemble boosting to generate potentially worse event severity and footprint (Thompson et al. 2025) or even higher antecedent conditions in autumn 2023, could be further extensions (Han et al. 2025). Consideration of human influences (e.g. land use change) and socio-economic
impacts (e.g. properties at risk of flooding) could potentially further contribute to impact-based attribution (Perkins-Kirkpatrick et al 2024).

Finally, large ensemble simulations suggest an increasing trend in winter half-year river flows across the 21st century. However, internal climate variability masks early detectability of a statistically significant climate-driven signal, although earlier signals are detectable for some catchments (e.g. central England and northern Scotland). This is consistent with previous studies showing that climate variability may conceal climate-driven trends in river flows (e.g. Wilby 2006, Chan et al 2025). The use of alternative SMILE simulations for hydrological modelling is subject to on-going work to consider climate model uncertainty. Robustness could also be enhanced with the use of alternative hydrological models.

In summary, we have demonstrated a framework for post-event analysis of hydrological extremes using UK river flooding in the winter half-year 2023/24 as a case study. A major advantage is that the framework goes way beyond standard event reporting that typically uses relatively short hydrological observations alone. Here, we combine extreme event attribution, ‘what if’ storylines and the use of initial-condition large ensembles. Additionally, the framework makes use of publicly available meteorological datasets (both observations and large ensemble climate model simulations) in conjunction with computationally efficient open-source hydrological models (e.g. the airGR R package). The methodology can potentially be applied to other hydrological extremes and other environments. However, while portable in principle, applications in other regions need to consider the wide variability in hydrological regimes. Regions or events dominated by other flood generation mechanisms (e.g. snowmelt) may require alternative process representation or hydrological model structures. High resolution and long meteorological observations are a key requirement for driving river flow reconstructions, but such datasets may not be readily available for all regions. However, internationally, there are many initiatives under way to extend and improve meteorological datasets, such as the C3S Data Rescue Service. Despite these constraints, there is clear potential for the framework presented here to be applied to other events in other regions to assist disaster risk reduction and climate change adaptation.

## Data availability statement

Observed river flow data for UK catchments can be obtained from the National River Flow Archive (NRFA) (https://nrfa.ceh.ac.uk/data/). Reconstructed monthly river flows for all 200 UK catchments for the 1850–2021 period using the GR2M hydrological are available at (DOI: https://doi.org/10.5281/zenodo.14988010). Daily

river flow reconstructions for 303 UK catchments for the 1891–2015 period are available from Smith et al (2018) (https://doi.org/10.5285/f710bed1-e564-47bf-b82c-4c2a2fe2810e). UK rainfall and temperature data are available from the HadUK-Grid dataset (Hollis et al 2019) and accessible via CEDA (https://catalogue.ceda.ac.uk/uuid/5a248096468640a6bfb0dfda8b018ac5/). Rainfall and temperature data for the CRCM5-LE are publicly available on the ClimEx website (www.climex-project.org/data-access/). River flow simulations across Great Britain driven by the CRCM5-LE are available from (https://doi.org/10.5281/zenodo.13990611). Daily SEAS5 hindcasts are available from the Climate Data Store (CDS) (https://doi.org/10.24381/cds.181d637e, Copernicus Climate Change Service, Climate Data Store 2018).

## Acknowledgments

This work is an output of the UK National Hydrological Monitoring Programme, and was supported by NERC through the UKCEH National Capability for UK Challenges Programme (NE/Y006208/1). Underlying research on the methodological development was funded by the NERC Project ‘Climate change in the Arctic-North Atlantic Region and Impacts on the UK’ [CANARI (NE/W004984/1)] and the ‘Co-Centre for Climate + Biodiversity + Water Programme’ funded by Science Foundation Ireland (SFI), Northern Ireland’s Department of Agriculture, Environment and Rural Affairs (DAERA) and UK Research and Innovation (UKRI) (22/CC/11103; NE/Y006496/1).

## Author contributions

Wilson C H Chan et al. 0000-0003-4296-3203
Conceptualization (lead), Formal analysis (lead), Investigation (lead), Visualization (lead), Writing – original draft (lead)

Lucy J Barker et al. 0000-0002-2913-0664
Conceptualization (supporting), Project administration (supporting), Supervision (supporting), Visualization (supporting), Writing – review & editing (supporting)

Davide Faranda et al. 0000-0001-5001-5698
Conceptualization (supporting), Methodology (supporting), Visualization (supporting), Writing – review & editing (supporting)

Jamie Hannaford et al. 0000-0002-5256-3310
Conceptualization (supporting), Funding acquisition (supporting), Methodology (supporting), Project administration (supporting), Supervision (lead), Writing – review & editing (supporting)
References

Arnell N W and Gosling S N 2016 The impacts of climate change on river flood risk at the global scale *Clim. Change* **134** 387–401

Athanase M, Sánchez-Benítez A, Monfort E, Jung T and Goessling H F 2024 How climate change intensified storm Boris' extreme rainfall, revealed by near-real-time storylines *Commun. Earth Environ.* **5** 676

Barker L, Hannaford J, Muchan K, Turner S and Parry S 2016 The winter 2015/2016 floods in the UK: a hydrological appraisal *Weather* **71** 324–33

Berghuys W R, Woods R A, Hutton C J and Sivapalan M 2016 Dominant flood generating mechanisms across the United States *Geophys. Res. Lett.* **43** 4382–90

Bevacqua E, Zappa G and Shepherd T G 2020 Shorter cyclone clusters modulate changes in European wintertime precipitation extremes *Environ. Res. Lett.* **15** 124005

Blöschl G et al 2020 Current European flood-rich period exceptional compared with past 500 years *Nature* **583** 560–6

Brigode P, Oudin L and Perrin C 2013 Hydrological model parameter instability: a source of additional uncertainty in estimating the hydrological impacts of climate change? *J. Hydrol.* **476** 410–25

Brunner M I and Slater L J 2022 Extreme floods in Europe: going beyond observations using reforecast ensemble pooling *Hydrol. Earth Syst. Sci.* **26** 469–82

Chan W C H, Shepherd T G, Facer-Childs K, Darch G and Arnell N W 2022 Storylines of UK drought based on the 2010–2012 event *Hydrol. Earth Syst. Sci.* **26** 1755–77

Chan W C, Arnell N W, Darch G, Facer-Childs K, Shepherd T G and Tanguy M 2024 Added value of seasonal hindcasts to create UK hydrological drought storylines *Nat. Hazards Earth Syst. Sci.* **24** 1065–78

Chan W, Tanguy M, Chevuturi A and Hannaford J 2025 Climate variability conceals emerging hydrological trends across Great Britain *J. Hydrol.* **660** 133414

Clarke B et al 2024 Climate change made the extreme 2-day rainfall event associated with flooding in Midleton, Ireland more likely and more intense *World Weather Attribution report*. (worldweatherattribution.org)

Copernicus Climate Data Store 2018 Seasonal forecast daily and subdaily data on single levels *Copernicus Climate Change Service (C3S) Climate Data Store (CDS)* (https://doi.org/10.24381/cds.181d637e)

Deser C and Phillips A S 2023 A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe *Nonlinear Process. Geophys. 30* 63–84

Dixon H, Hannaford J and Fry M J 2013 The effective management of national hydrometric data: experiences from the United Kingdom *Hydrol. Sci. J.* **58** 1383–99

Faghih M and Brissette F 2023 The role of internal climate variability on future streamflow projections *J. Hydrol.* **625** 130101

Fang B, Bevacqua E, Rakovec O and Zscheischler J 2024 An increase in the spatial extent of European floods over the last 70 years *Hydrol. Earth Syst. Sci.* **28** 3755–75

Faranda D et al 2024 ClimaMeter: contextualizing extreme weather in a changing climate *Weather Clim. Dyn.* **5** 959–83

Faranda D, Ginesta M, Alberti T, Coppola E and Anzidei M 2023a Attributing Venice Acqua Alta events to a changing climate and evaluating the efficacy of MoSE adaptation strategy *npj Clim. Atmos. Sci.* **6** 181

Faranda D, Pascale S and Bulut B 2023b Persistent anticyclonic conditions and climate change exacerbated the exceptional 2022 European-Mediterranean drought *Environ. Res. Lett.* **18** 034030

Fraedrich K and Müller K 1992 Climate anomalies in Europe associated with ENSO extremes *Int. J. Climatol.* **12** 25–31

Gillett N P et al 2022 Human influence on the 2021 British Columbia floods *Weather Clim. Extremes* **36** 100441

Goulart H M D, Benito Lazaro I, van Garderen L, van der Wiel K, Le Bars D, Koks E and van den Hurk B 2024 Compound flood impacts from Hurricane Sandy on New York City in climate-driven storylines *Nat. Hazards Earth Syst. Sci.* **24** 29–45

Griffin A, Kay A L, Sayers P, Bell V, Stewart E and Carr S 2024 Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections *Hydrol. Earth Syst. Sci.* **28** 2635–50

Griffin A, Vesuviano G, Wilson D, Sefton C, Turner S, Armitage R and Suman G 2025 Putting the English flooding of 2019–2021 in the context of antecedent conditions *J. Flood Risk Manage.* **18** e70016

Han L, Merz B, Nguyen V D, Guse B, Samaniego L, Schröter K and Vorogushyn S 2025 Recombining past event precipitation and antecedent catchment states generates unprecedented floods *Commun. Earth Environ.* **6** 692

Hannaford J, Armitage R, Smith R, Crane E and Clemas S 2024 *Hydrological summary for the United Kingdom: March 2024* (Wallingford, UK, UK Centre for Ecology & Hydrology) p 12 (available at: https://nora.nerc.ac.uk/id/eprint/537317)

Hawkins E et al 2023 Millions of historical monthly rainfall observations taken in the UK and Ireland rescued by citizen scientists *Geosci. Data J.* **10** 246–61

Heinrich D, Stephens E and Coughlan De Perez E 2024 More than magnitude: towards a multidimensional understanding of unprecedented weather to better support disaster management *Water Secur.* **23** 100181

Hollis D, McCarthy M, Kendon M, Legg T and Simpson I 2019 HadUK-Grid—A new UK dataset of gridded climate observations *Geosci. Data J.* **6** 151–9

Jézéquel A, Yiou P and Radanovics S 2018 Role of circulation in European heatwaves using flow analogues *Clim. Dyn.* **50** 1145–59

Johnson S J et al 2019 SEAS5: the new ECMWF seasonal forecast system *Geosci. Model Dev.* **12** 1087–117

Kay A L 2016 A review of snow in Britain: the historical picture and future projections *Prog. Phys. Geogr.* **40** 676–98

Kay A L, Booth N, Lamb R, Raven E, Schaller N and Sparrow S 2018 Flood event attribution and damage estimation using national-scale grid-based modelling: winter 2013/2014 in Great Britain *Int. J. Climatol.* **38** 5205–19

Kay A L, Crooks S M, Pall P and Stone D A 2011 Attribution of autumn/winter 2000 flood risk in England to anthropogenic climate change: a catchment-based study *J. Hydrol.* **406** 97–112

Kay A, Dunstone N, Kay G, Bell V and Hannaford J 2024 Demonstrating the use of UNSEEN climate data for hydrological applications: case studies for extreme floods and droughts in England *Nat. Hazards Earth Syst. Sci.* **24** 2953–70

Kelder T et al 2025 How to stop being surprised by unprecedented weather *Nat. Commun.* **16** 2382

Kelder T, Marjoribanks T I, Slater L J, Prudhomme C, Wilby R L, Wagemann J and Dunstone N 2022 An open workflow to gain insights about low-likelihood high-impact weather events from initialized predictions *Meteorol. Appl.* **29** e2065

Kendon M et al 2024 State of the UK climate 2023 *Int. J. Climatol.* **44** 1–117

Kew S et al 2024 Autumn and winter storms over UK and Ireland are becoming wetter due to climate change *World Weather Attribution report*. (worldweatherattribution.org) (https://doi.org/10.1016/j.mex.2024.102765)

Leduc M et al 2019 The ClimEx project: a 50-member ensemble of climate change projections at 12-km resolution over Europe and Northeastern North America with the Canadian regional climate model (CRCM5) *J. Appl. Meteorol. Climatol.* **58** 663–93
Lin Y C, Jenkins S F, Chow J R, Biass S, Woo G and Lallemant D 2020 Modeling downward counterfactual events: unrealized disasters and why they matter *Front. Earth Sci.* 8 575048

Merz B, Nguyen V D, Guse B, Han L, Guan X, Rakovec O, Samaniego L, Ahrens B and Vorogushyn S 2024 Spatial counterfactuals to explore disastrous flooding *Environ. Res. Lett.* 19 044022

Met Office 2023 Seasonal Assessment - Autumn 2023 (available at: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/weather/learn-about/uk-past-events/summaries/uk_climate_summary_autumn_2023.pdf) (Accessed 01 September 2025)

Met Office 2024 UK storm season 2023/24 (available at: https://weather.metoffice.gov.uk/warnings-and-advice/uk-storm-centre/uk-storm-season-2023-24) (Accessed 01 September 2025)

Muchan K, Lewis M, Hannaford J and Parry S 2015 The winter storms of 2013/2014 in the UK: hydrological responses and impacts *Weather* 70 55–61

Murphy C et al 2020 Multi-century trends to wetter winters and drier summers in the England and Wales precipitation series explained by observational and sampling bias in early records *Int. J. Climatol.* 40 610–9

O'Connor P, Murphy C, Matthews T and Wilby R L 2021 Reconstructed monthly river flows for Irish catchments 1766–2016 *Geosci. Data J.* 8 34–54

Ommer J, Neumann J, Kalas M, Blackburn S and Cloke H L 2024 Surprise floods: the role of our imagination in preparing for disasters *Nat. Hazards Earth Syst. Sci.* 24 2633–46

Perkins-Kirkpatrick S E et al 2024 Frontiers in attributing climate extremes and associated impacts *Front. Clim.* 6 1455023

Richardson D, Fowler H J, Kilsby C G and Neal R 2018 A new precipitation and drought climatology based on weather patterns *Int. J. Climatol.* 38 630–48

Scussolini P et al 2024 Challenges in the attribution of river flood events *WIREs Clim. Change* 15 e874

Sefton C, Muchan K, Parry S, Matthews B, Barker L J, Turner S and Hannaford J 2021 The 2019/2020 floods in the UK: a hydrological appraisal *Weather* 76 378–84

Shepherd T G 2014 Atmospheric circulation as a source of uncertainty in climate change projections *Nat. Geosci.* 7 703–8

Shepherd T G et al 2018 Storylines: an alternative approach to representing uncertainty in physical aspects of climate change *Clim. Change* 151 555–71

Sillmann J, Shepherd T G, van den Hurk B, Hazeleger W, Martius O, Slingo J and Zscheischler J 2021 Event-based storylines to address climate risk *Earth's Future* 9 e2020EF001783

Smith K A, Tanguy M, Hannaford J and Prudhomme C 2018 Historic reconstructions of daily river flow for 303 UK catchments (1891-2015) *NERC Environmental Information Data Centre* (https://doi.org/10.5285/f710bed1-e564-47bf-b82c-4c2a2fe2810e)

Stott P A et al 2016 Attribution of extreme weather and climate-related events *WIREs Clim. Change* 7 23–41

Thompson V, Coumou D, Beyerle U, Ommer J, Cloke H L and Fischer E 2025 Alternative rainfall storylines for the Western European July 2021 floods from ensemble boosting *Commun. Earth Environ.* 6 427

Thompson V, Dunstone N J, Scaife A A, Smith D M, Slingo J M, Brown S and Belcher S E 2017 High risk of unprecedented UK rainfall in the current climate *Nat. Commun.* 8 107

Thompson V, Philip S Y, Pinto I and Kew S F 2024 The influence of the Atlantic multidecadal variability on Storm Babet-like events *EGUsphere* 2024 1–15

Wilby R L 2006 When and where might climate change be detectable in UK river flows? *Geophys. Res. Lett.* 33 L19407

Winter M, Le Blancq F, Le Maistre A and Plummer R 2024 Storm Ciarán—an exceptionally severe windstorm in the Channel Islands *Weather* 79 wea.7631

Woo G 2019 Downward counterfactual search for extreme events *Front. Earth Sci.* 7 340