Hail Frequency Europe: ERA5, AR-CHaMo and ESRS E1-9

„Hail projection for 2050" sounds like a solid climate scenario, but it is not. As a sub-grid convective phenomenon, hail cannot be directly represented in CMIP5 and CMIP6 global models, because hail cells form on a 1 to 20 km scale within minutes, while global models run on 50 to 150 km grids with parameterised convection. Anyone who still needs hail statements for industrial sites in Europe has two credible routes: observational data (ESWD, DWD radar, GDV) and proxy-based reanalyses such as the AR-CHaMo model of Battaglioli et al. on ERA5. This authority article maps the current scientific state, the methodological boundary, the regional hotspots across Europe (with Germany as a worked case) and the ESRS-E1-9-compliant assessment path for CSRD-reporting industrial sites.

AR-CHaMo on ERA5: Battaglioli et al. and the global trend

The Swiss meteorologist and ESSL researcher Francesco Battaglioli, together with colleagues, developed AR-CHaMo (Additive Regression Convective Hazard Model). The statistical model predicts the occurrence of large hail (≥ 2 cm) and very large hail (≥ 5 cm) based on atmospheric indicators. It was trained on roughly 24 million lightning observations and over 44,000 hail reports, applied to the hourly ERA5 reanalysis of the ECMWF on a 0.25° × 0.25° grid.

The key publication for practitioners is „Contrasting trends in very large hail events and related economic losses" (Battaglioli et al., Nature Geoscience, 2025). Three main findings for Europe and Germany:

• From 1950 to 2021, the ERA5-AR-CHaMo models show significant increases in hail across large parts of Europe, primarily driven by rising moisture in the lowest atmospheric layers.
• The strongest increase worldwide occurs in northern Italy: very large hail (≥ 5 cm) is now (2012–2021) roughly three times more frequent than in the 1950s.
• For Europe overall, the strongest global rise in very large hailstones is observed, in stark contrast to the southern hemisphere, where decreases are documented.

A methodologically important sub-result: the CAPE energy above the minus 10 °C isotherm (not classical CAPE) proved to be a universally superior predictor for large hail. Anyone making hail statements in a climate context cannot get around this parameter anymore.

ERA5 itself is the fifth generation of global climate reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). Hourly data points since 1940, 31 km grid, over 100 atmospheric levels. Unlike pure observation networks, ERA5 combines physical models with historical measurements and is spatially complete. That is what makes it valuable for hail climatology in regions with sparse observational coverage.

KIT studies, Frontiers 2026 and Kahraman et al.

German hail climatology is essentially carried by the IMK-TRO at KIT Karlsruhe. Three key studies:

• Kunz, Sander & Kottmeier (2009, Int. J. Climatol.): Analysis of hail damage days in Baden-Württemberg 1974–2003. Significant increase in damage days despite constant thunderstorm frequency. Convection indices based on radiosonde data explain over 55 per cent of the annual variance.
• Mohr & Kunz (2013, Atmos. Res.): „Recent trends and variabilities of convective parameters relevant for hail events in Germany and Europe". Confirms that near-surface temperature and humidity indices show positive trends, whereas indices from higher atmospheric layers are neutral or negative.
• Puskeiler, Kunz & Schmidberger (2016, Atmos. Res.): First comprehensive hail statistics for Germany based on C-band radar data 2005–2011. Clear north-south gradient, several hotspots in the lee of low mountain ranges.

Frontiers in Environmental Science (2026, KIT/climXtreme): 15,577 potential hail tracks from 20 summer half-years (2005–2024) on a 3D C-band radar dataset. Spatially a clear north-south gradient. Highest hail frequency south of Stuttgart and in the Bavarian Alpine foothills. Temporally no significant national trend. Spatially differentiated: significant increase along the Baden-Württemberg/Bavaria border and south-east of Munich, significant decrease across large parts of northern and western Germany. Scandinavian blocking patterns offer a teleconnection explanation.

The seemingly contradictory counter-finding comes from Kahraman et al. (Nature Communications, September 2025), Met Office and British universities. Using km-scale climate simulations under a high-emission scenario (RCP 8.5, +5 °C), the picture for central Europe shifts:

• Severe hail (≥ 2 cm) may become rarer, because a higher freezing level and weaker large-scale weather patterns inhibit hail formation.
• Very large hail (≥ 5 cm) may still increase, through more tropical storm types with very strong updrafts, particularly in southern Europe.
• For central Europe and Germany this means, under an extreme scenario, regionally fewer but more intense events.

These findings do not contradict the historical ERA5 trends, they describe a possible future under strong warming. For site-level risk assessment both views belong in the analysis, because they address different time horizons.

Why CMIP6 cannot project hail directly and which proxies help

CMIP6 global models typically have horizontal resolutions of 50 to 150 km and use parameterised convection. Hail cells form on horizontal scales of 1 to 20 km within minutes, far below grid cell size. Three consequences:

1. Convection parameterisation cannot realistically represent either hail growth or melting processes during fall.
2. Spatial resolution is insufficient for orographically triggered convergence (Black Forest, Swabian Alb, Alpine foothills).
3. Hail statistics are not directly output by CMIP6.

Those who still need climate projections for hail relevance work with proxies:

The literature converges: a combination of CAPE-based instability and wind shear reproduces historical hail climatologies better than single parameters. AR-CHaMo outperforms most composites at medium and long forecast horizons. For an ESRS-E1-9 assessment the choice matters, because it carries the justification for the uncertainty range. Anyone diving deeper into the difference between direct projections and proxy-based statements will find the methodological frame in our CORDEX vs. CMIP6 data choice guide and the appropriate methodology framework in ISO 14091 in practice for banks and insurers.

Datasets for site-level assessment

Four data sources are practically relevant for German industrial sites:

• ESSL European Severe Weather Database (ESWD): Over 310,000 quality-controlled reports for large hail, gusts, tornadoes and heavy precipitation. Operational since 2006, with historical data before. DWD is a cooperation partner. Limitation: inhomogeneous report density, population-dependent, lower coverage in northern Germany and rural regions. Access via ESSL membership.
• DWD radar network: 17 C-band Doppler radars, complete national coverage. A homogenised archive dataset since 2005, basis of the KIT studies. Available as RADOLAN/RADKLIM for precipitation, HAIL-Composite for hail probability, and convection indices from radiosonde stations in Essen, Meiningen, Munich and Lindenberg.
• GDV Naturgefahrenreport: Insured loss data since 1973, price-adjusted, with regional breakdown. Around 70 tables per annual report. The GDV actively supports research access.
• Insurer-side sources: Insurers such as Verti publish annual hail atlases with state and city rankings. Usable for first orientation, too unsystematic for ESRS reporting.

Meteomedia/Kachelmann operates under the meteosol® brand in cooperation with Vereinigte Hagel, providing agriculture-focused hail damage monitoring. For industrial site assessment its informational value is limited, because no systematic quality control to scientific standards is documented.

Hail hotspots in Germany

The regional evidence base from radar, insurance and model data:

The strongest evidence rests on the 20-year DWD radar dataset, analysed in Frontiers (2026). The north-south gradient is consistent across all studies. The significant increase along the BW/Bavaria border is a newer finding with high statistical robustness. For site managers this means: an industrial site choice in the Allgäu or south of Munich should account for the hail trend with a 30 to 40 per cent higher probability of very large hailstones by the end of the century (under a moderate warming scenario).

GDV loss statistics 2023 to 2025

Three consecutive years with marked variation:

• 2023: Total natural hazard losses 5.6 billion euros. Of which storm/hail in property insurance 2.7 billion, in motor 1.3 billion (fourth highest value ever). Hotspots in June (740 million) and August (1.5 billion). In Kassel more than every third comprehensive-insured vehicle was damaged, the highest claim frequency since 1984.
• 2024: 5.7 billion overall, storm/hail property 1.8 billion. Highest state losses in Bavaria and Baden-Württemberg, each around 1.6 billion (including the June flooding).
• 2025: Marked decline. Property natural catastrophe losses 1.4 billion nationally, 3 billion less than 2024. Of which storm/hail 1.0 billion. In Bavaria only 118 million, less than a tenth of the previous year.

Regional consistency: Baden-Württemberg, Bavaria and Thuringia lead the hail loss ranking in almost every year. Northern Germany (Mecklenburg-Vorpommern, Schleswig-Holstein, Bremen) sits regularly at the lower end. Average loss per event in BW around 3,300 euros, Bavaria around 3,000, both clearly above the national average.

Peak years such as 2013 or 2021 (Europe-wide) caused hail losses in Germany in the double-digit billion range. Northern Italy reached a record single-event loss of 6 billion US dollars in 2023, an impressive confirmation of Battaglioli's trend findings.

ESRS-E1-9-compliant assessment path for hail

ESRS E1-9 requires the quantification of financial effects of material physical climate risks on assets, separated into acute and chronic risks, with affected asset values disclosed across short-, medium- and long-term time horizons. ESRS E1-2 (AR 10–13) requires a climate scenario analysis as the basis. Where direct projections are missing, the framework explicitly allows analogies, proxy indicators and expert judgement, provided the approach is methodologically justified and transparently documented.

For hail at European industrial sites a five-step path:

1. Historical hail exposure class from geocoding against the ESWD density map, DWD radar hail climatology (Puskeiler/Kunz 2016, Frontiers 2026) and GDV regional data. Output: an ordinal class (low, medium, high).
2. Trend adjustment by overlay with the AR-CHaMo ERA5 trend for the region (1950–2021). Southern Germany: trend amplifier. Northern Germany: trend dampener or neutral.
3. Proxy scenario analysis using CAPE and Lifted Index trends from CMIP6 for the chosen SSP scenario, as a proxy for future hail exposure change (mid-term to 2050, long-term to 2100).
4. Financial exposure across building assets, outdoor infrastructure (PV modules, cooling plants, outdoor storage), business interruption risks. Linked to insurance cover and deductibles.
5. Documentation for E1-9 with justification of the datasets (ERA5/AR-CHaMo, ESWD, GDV), the uncertainty range and references to peer-reviewed literature. Explicitly state that direct CMIP6 hail projection is methodologically unavailable and why proxies are adequate.

This approach also offers a clean transition to the ISO 14091 in practice methodology and to insurance and coverage gaps. Anyone building an audit-proof E1-9 assessment for CSRD-reporting industrial companies cannot shortcut the path with the words „hail projection 2050", but must make the methodological limits explicit, and that is precisely what auditors reward.

Frequently asked questions

Further resources

• Climate risk analysis for companies: Methodology to ISO 14091
• ISO 14091 in practice: What banks and insurers expect
• CORDEX EUR-11 vs. CMIP6: Climate data choice for risk analysis
• RCP and SSP climate data: A full guide
• Climate risks and insurance gaps
• Physical vs. transition climate risks