Contact Authors
Tommaso Alberti (INGV, Italy) tommaso.alberti@ingv.it – IT/EN
Mireia Ginesta (Oxford Sustainable Law Programme, University of Oxford) - mireia.ginesta@smithschool.ox.ac.uk CA/SP/EN
Carmen Álvarez-Castro (UPO, Spain) - mcalvcas@upo.es - SP/EN/IT
Haosu Tang (University of Sheffield, UK) – haosu.tang@sheffield.ac.uk – ZH/EN
Davide Faranda (IPSL-CNRS, France) - davide.faranda@lsce.ipsl.fr - EN/FR/IT
Citation
Alberti, T., Ginesta, M., Alvarez-Castro, M. C., Tang, H., & Faranda, D. (2026). Heavy precipitation in Storm Nils mostly strengthened by human-driven climate change. ClimaMeter, Institut Pierre Simon Laplace, CNRS. https://doi.org/10.5281/zenodo.18623164
Press Summary
Storms similar to Nils are up to 4 mm/day (up to 10%) wetter and locally up to 3km/h (up to 5%) windier over France, Spain and Portugal in the present than they would have been in the past.
This event was associated with very rare meteorological conditions.
We mostly ascribe the stronger winds and heavy rains of Storm Nils to human-driven climate change and natural climate variability likely played a minor role.
Event Description
On February 11, 2026, Storm Nils swept across parts of Western Europe, bringing violent winds, heavy rain, flooding risks, and widespread disruption from southern France into Spain. In France, Nils firstly made landfall along the Atlantic coast, with extremely strong winds reaching up to 162 km/h over the coast and other powerful gusts of 132 km/h in Toulouse. Heavy rainfall caused rivers like the Garonne to rise rapidly, more than doubling in some areas over 48 hours. The French Alps saw an exceptional avalanche risk (level 5) in Savoie as a result of the storm’s passage, forcing the closure of French Ski Resorts. Authorities issued red alerts for departments including Gironde, Lot‑et‑Garonne, Aude and Savoie, while more than 30 other departments were under orange warnings for strong winds, flooding and coastal submersion. Storm Nils left approximately 850,000 homes without power, particularly in Nouvelle‑Aquitaine and Occitanie, and tragically killed at least one person when a falling tree branch struck a lorry driver. Widespread transport disruption followed: roads were blocked by debris, rail services were affected, and some ferry links were interrupted. Schools in affected departments such as Aude and Pyrénées‑Orientales were closed, and several evacuations took place in flood‑threatened zones. Authorities urged residents to stay indoors, limit travel and follow safety instructions as recovery efforts continued.
Across the Iberian Peninsula, Storm Nils compounded an already volatile weather situation. In Spain, the storm triggered widespread weather alerts and emergency measures. Authorities in Catalonia suspended school classes, sporting events and some non‑urgent medical services as powerful winds battered the region. Gusts exceeded 105 km/h, reaching 140 km/h in parts of Barcelona province, making it one of the most intense wind episodes there in years, and resulting in multiple injuries and significant transport disruption. At Barcelona’s El Prat Airport, numerous flights were cancelled or diverted due to crosswinds and dangerous conditions. Roads and rail services across Catalonia faced closures or restrictions due to fallen trees and debris, and mobile emergency alerts urged residents to shelter indoors and avoid unnecessary travel. Storm Nils also produced heavy rain and flooding risks further south and west in Spain, particularly in Andalusia and other regions threatened by saturated rivers and rising water levels after recent weeks of persistent storms. Meteo‑alarm systems were activated for parts of northern Spain like Galicia, the Basque Country and Cantabria, where high waves and wind led to red‑level warnings along the coast. Emergency services responded to hundreds of incidents across the country as riverbanks rose and flooding concerns grew.
On 12 February, Nils reached also Italy with strong winds across Sardinia, reaching 139 km/h at Punta Sebera and over 120 km/h in several inland areas. Heavy rain caused localized flooding in Rome and the Castelli Romani, with up to 65 mm in a few hours, paralyzing traffic and requiring emergency interventions. Coastal and island areas, experienced extremely dangerous seas, while southern regions like Calabria faced potential alluvial floods of 200–300 mm in a single night.
The meteorological conditions were characterized by persistent and strong negative surface pressure anomalies, exceeding -20 hPa over the English channel and extending towards Europe. Near-surface temperature anomalies exhibited a clear pattern over Western to Central Europe, with positive values locally exceeding +5 °C across large parts of the affected region. Daily precipitation totals locally exceeded 50 mm, with the most intense rainfall concentrated along southern France and Portugal. Sustained near-surface wind speeds locally reached up to 80 km/h during the event over the French Atlantic coast. A pronounced easterly flow played a key role in enhancing moisture advection from the Atlantic Ocean, contributing to prolonged and intense rainfall over France, Spain, and Portugal.
Climate and Data Background for the Analysis
The IPCC AR6 Chapter 11 states that climate change affects storms in Europe, with negative repercussions that are exacerbated by rising sea levels and heavy precipitation. With a global warming level of 2 °C or higher, a slightly increased frequency and amplitude of extratropical cyclones, strong winds and extratropical storms is projected for western Europe with medium confidence (IPCC AR6 WGI Chapter 12).
Observations show that extreme precipitation and pluvial floods have been increasing in Western Europe. Climate models — from global to high resolution, kilometre-scale convection-permitting models — show a strong consensus that these events will become more frequent and intense as global temperatures rise by 2 °C or more. (IPCC AR6 WGI Chapter 12). Studies, such as Ginesta et al. (2024), confirm that the intensity of recent storms, including both wind speed and precipitation, is likely to increase in the most impacted regions of Europe.
Our analysis approach rests on looking for weather situations similar to those of the event of interest having been observed in the past. For this event we have
ClimaMeter Analysis
We analyze here (see Methodology for more details) how events similar to the meteorological conditions leading to Storm Nils have changed in the present (1988–2025) compared to what they would have looked like if the event had occurred in the past (1950–1987) in the region [20°W–20°E, 36°N–60°N]. From the surface-pressure changes it appears that storms like Nils are now shallower in the present than in the past. Temperature changes show a robust warming signal, with increases of about +1 to +2 °C over Portugal, Spain, and Southern France, consistent with a warmer background state during the event. Precipitation changes are not uniform but suggest locally wetter conditions, particularly in the most affected region by the Storm Nils, i.e., south-eastern France and Northern Portugal. Wind speed changes also show locally stronger winds in the same regions, likely producing enhanced easterly to southeasterly low-level flow, favouring stronger onshore wind impacts and more efficient moisture advection toward southern France. Similar past events indicate a major seasonal shift from December to January. Changes in urban areas (Bordeaux, Toulouse, Perpignan) are consistent with the regional signal, showing warmer conditions, wetter intensification, and stronger winds compared to past analogues, implying increased exposure to rainfall- and wind-related damage and to compounding coastal hazards such as waves and surge during comparable configurations today.
Finally, we find no evidence for a detectable influence of major modes of natural climate variability — such as the Atlantic Multidecadal Oscillation, the Pacific Decadal Oscillation or El Nino Southern Oscillation — on this event. This means that the changes we see in the event compared to the past have been strengthened due to human-driven climate change.
Conclusion
Based on the above, we conclude that meteorological conditions associated with Storm Nils were stronger than comparable past events, with precipitation increased by up to 4 mm/day (≈ 10%). We interpret this as an event driven by very rare meteorological conditions whose intensity has been amplified by human-driven climate change, through a warmer background state.
NB1: The following output is specifically intended for scientists and contain details that are fully understandable only by reading the methodology described in Faranda, D., Bourdin, S., Ginesta, M., Krouma, M., Noyelle, R., Pons, F., Yiou, P., and Messori, G.: A climate-change attribution retrospective of some impactful weather extremes of 2021, Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, 2022.
NB2: Colorscales may vary from the ClimaMeter figure presented above.
The figure shows the average of surface pressure anomaly (msl) (a), average 2-meter temperature anomalies (t2m) (e), cumulated total precipitation (tp) (i), and average wind speed (wspd) in the period of the event. Average of the surface pressure analogs found in the counterfactual (b) and factual periods (c), along with corresponding 2-meter temperatures (f, g), cumulated precipitation (j, k), and wind speed (n, o). Changes between present and past analogues are presented for surface pressure ∆slp (d), 2-meter temperatures ∆t2m (h), total precipitation ∆tp (i), and wind speed ∆wspd (p): color-filled areas indicate significant anomalies with respect to the bootstrap procedure. Violin plots for past (blue) and present (orange) periods for Quality Q analogs (q), Predictability Index D (r), Persistence Index Θ (s), and distribution of analogs in each month (t). Violin plots for past (blue) and present (orange) periods for ENSO (u), AMO (v), and PDO (w). Number of the analogues occurring in each subperiod (blue) and linear trend (black). Values for the peak day of the extreme event are marked by a blue dot. Horizontal bars in panels (q,r,s,u,v,w) correspond to the mean (black) and median (red) of the distributions. (x) Number of analogues found in sub periods when analogues are searched in the whole reanalysis period.