The planning and operation of electricity systems with high shares of variable intermittent renewable energies (VRE) require a solid understanding of weather impacts, which are becoming increasingly influenced by climate change and associated extreme events. However, there is a lack of standardized methodologies for identifying extreme events and systematically assessing climate change impacts on power systems. This study addresses this gap by applying a residual load (RL)-based framework that integrates climate projections with indicators of a VRE-dominated future power system, enabling the systematic detection and analysis of extreme events. RL is defined as the difference between electricity demand and VRE generation. The proposed RL-based framework assesses climate-change impacts on power systems and identifies extreme events under future climate scenarios. This is achieved through a multi-annual analysis combining a decarbonization pathway with moderate and strong climate-change scenarios. The analysis evaluates changes in specific RL indicators across geographical scales, including the EU, and Austria as a country case. In Austria, stronger climate change shifts peak periods of residual load (PPRL) towards summer, indicating a growing influence of heatwaves. At the same time, PPRL frequency increases to up to six events per year, while maximum event durations decrease from 46–53 to 33–34 days. At the EU level, PPRL events are less frequent (1–2 per year) and shorter (around 10 days, with maximum durations of 20 days). This highlights the importance of strong interconnection within the European power system, as cross-border exchanges can support regions and countries during extreme-event periods.

Urban climate is shaped not only by processes within city centres but also by land-use changes and development dynamics in surrounding suburban areas. High-resolution urban climate models such as the PALM model system enable the explicit representation of urban morphology and surface characteristics and are therefore well suited to investigate how urban densification and soil sealing modify local microclimatic conditions and potentially amplify urban heat island (UHI) effects. While microclimatic impacts of specific urban development projects within city borders already received wide attention in different studies, the microclimatic interrelations of large-scale suburban developments based on different scenarios still require more detailed evaluation in the research community.This contribution presents first results from the INTERFERE project, where the PALM model system is applied to investigate how business-as-usual (BAU) and future regional development pathways influence suburban climate conditions around the city of Graz (Austria) by addressing suburban growth, infrastructure expansion, and associated land-use changes. Two spatial development scenarios are examined: 1) A BAU scenario assumes continuation of current planning practices, including full utilisation of designated building land that has not yet been developed, limited targeted densification, expansion of transport infrastructure, and no reduction of existing development reserves. 2) In contrast, a climate-sensitive planning scenario (Best Practice - BP) follows similar development constraints but emphasises compact urban development and targeted densification—particularly around public transport corridors—enhanced greening measures, and reduced land take through more efficient use of existing reserves. The spatial development scenarios are derived from official zoning and regional planning instruments and are developed by a local spatial planner, who is actively involved in supra-regional and regional planning processes in Styria to ensure realistic scenarios and policy-relevant future pathways. The resulting land-use configurations are than translated into the PALM model system.The PALM simulations are driven by boundary conditions from high-resolution mesoscale modelling (WRF coupled with the Town Energy Budget model, TEB; Trimmel et al., 2021) at 300 m resolution, provided within the project. Since the mesoscale forcing explicitly accounts for urban structures and urban energy exchange processes, it provides a substantially more realistic representation than forcings from large-scale reanalyses products. For each scenario, a 30-hour heatwave episode, including a spin-up phase, is simulated based on a historical extreme summer event.By explicitly linking regional spatial planning scenarios with high-resolution microclimate modelling, this study provides new insights into how suburban development patterns influence heat exposure and thermal comfort. In a next step, the results will be discussed with local mayors and key stakeholders to identify and derive appropriate counteracting measures, which will subsequently be assessed through additional simulations. The findings, to be presented at this conference, aim to support evidence-based spatial planning and climate adaptation strategies under increasing heat stress.

Wildfires pose an increasing risk in many parts of the world. In Central Europe and especially in the Alpine region, uncontrolled wildfires are becoming a relevant issue due to climatic and socio-economic changes. Fire modelling in these areas requires tools that can predict fire behavior and spread under the specific environmental conditions. This study aims to apply the PhyFire wildfire simulation model-originally developed for Mediterranean environments-to Central European conditions, refine its parameters and assess its applicability for a case study in Austria. PhyFire, a 2D model with partial 3D capabilities including wind modeling, is based on the principles of mass and energy conservation. A variety of input datasets, fuel-type maps (EFFIS, firEUrisk, and a manually derived high-resolution map), meteorological and adjusted fuel-type-dependent parameters (including newly specified values for forest fuel-type classes) were tested. Additionally, the parameter pfRad was defined for each fuel type to compute flame radiation and energy flux to surrounding fuels. Therewith ignition and spread representation under Central European conditions were improved. Model outputs were compared against observed temporal and spatial spread patterns based on operational records. The simulation accuracy was evaluated by calculating over- and underestimation and computing the S & oslash;rensen-Dice coefficient for each inputdata combination. The model performed well overall but was overly sensitive to slope and wind direction. The study confirms PhyFire's potential for Central European wildfire modeling but highlights the need for a careful selection of the input data and the required parameter adjustments for increasing predictive reliability. For a broader application more recent high-resolution fuel data are essential to enhance predictive power and model accuracy.

This study investigates weather-driven stress events in the Austrian and Central European power system by combining an in-depth energy system analysis of two historical years with a climatological assessment of event likelihoods. The year 2017 exemplifies a pronounced dark doldrum period, characterised by persistently low wind and solar capacity factors, while 2022 represents an heatwave-induced hydropower drought with prolonged high temperatures, reduced precipitation and decreased river flows. Droughts of electricity infeed from variable renewable energy sources are identified using multiscale moving average capacity factors and a multi-threshold framework, enabling a power-systemrelevant definition of thresholds. To assess the future occurrence of similar events, percentile-based meteorological thresholds derived from historical conditions are applied to a subset of the CMIP6 climate model ensemble across different Global Warming Levels. Results indicate a decline in dark doldrums (up to $-77 \%$) and an increase in heatwave-related events (up to $\boldsymbol{+} \mathbf{1 6 3 \%}$) at GWL- $\mathbf{4. 0}{ }^{\circ} \mathrm{C}$, signalling a shift in system stress.

In a simulation study future development of forest resources in five Central European countries (Germany, Czechia, Slovakia, Austria, Slovenia) was explored. Initial state of 19.4 mill. ha forest was mapped at 1 × 1km resolution based on national forest inventory data, CORINE landcover types and a gap-filling algorithm. The forest ecosystem model PICUS v1.5 was employed to simulate forest resources in a set-up of management (no management, current and adaptive management) and climate scenarios (historic-current climate, three transient climate change scenarios) until 2099, including bark beetle disturbances. Particular focus was on transformation of Norway spruce forests. When temperature increases of +3-4 °C (end of 21st century) coincide with a decrease in summer precipitation volume stocks could not be maintained with current management due to reduced productivity and increased tree mortality, particularly in Norway spruce forests. In the period 2050-2099 adaptive management could reduce the loss in mean standing stock compared to continuation with current management practices by up to 585 mill. m3. Total harvests in the long run were moderately lower in adaptive management scenarios under climate change conditions of GWL2.6 and GWL4.1 (up to -8%). Under severe climate change (GWL4.7) adaptive management generated higher harvests than continuation with current management. The harvested volume of Norway spruce under adaptive management in 2050-2099 was reduced depending on management and climate. Losses ranged from -24.02 mill. m3 yr-1 to -8.34 mill. m3 yr-1. At least partially, the lost Norway spruce harvest volume could be substituted by Douglas fir in the future.

The Alpine water cycle is profoundly reshaped by anthropogenic climate change. Shifts in spatial distribution, timing and intensity of precipitation, together with rising winter temperatures, are modifying snowfall and snowmelt regimes and alter snowpack structure. Consequently, avalanche risk and regionally assessed avalanche hazard levels are likely to undergo significant changes under ongoing climate warming. In this study, we investigate potential future changes in avalanche hazard levels in the Austrian Alps under the Global Warming Levels 2°C, 3°C and 4°C. Our projections are based on the latest generation of EURO-CORDEX models, analysed using a machine-learning algorithm trained on ERA5 meteorological reanalysis data. For training, we use a novel multi-year observational data set compiled from regional avalanche hazard assessments and broadcast teletext archives. Within this data set, individual mountain ranges are grouped into response units that show similar avalanche hazard responses to different weather patterns. This allows us to apply a pattern-recognition algorithm that relates circulation analogues to specific changes in avalanche hazard levels. The algorithm is first validated against long-term observations in Tyrol using the full ERA5 data record and is subsequently applied to the future climate projections.The results of this study show when, and to what extent, the effects of human-caused climate change exceed the interannual and decadal variability of avalanche hazard over the course of the 21st century. The emergence of a climate change signal has important implications for hazard assessment and risk management in the Alpine region. It furthermore highlights critical warming thresholds beyond which current forecasting practices may require substantial revision.

This study investigates how climate-change-driven sea surface temperature (SST) anomalies influenced the extreme precipitation and moisture sources associated with Storm Boris. Between 12 and 16 September 2024, this slow-moving Vb-like cyclone produced exceptional rainfall and severe flooding across Central Europe, including more than 350 mm of accumulated precipitation within five days in parts of Austria. While previous studies have emphasized the importance of large-scale dynamics, blocking, and strong ascent for this event, the role of SST anomalies in the surrounding basins and their effect on moisture supply remain less quantified. Here, we focus on how SST changes in the Mediterranean, Black Sea, and Atlantic modified both moisture sources and precipitation intensity during Boris.To address this, we perform a set of sensitivity experiments with the Weather Research and Forecasting (WRF) model in which SSTs in the Mediterranean, Black Sea, and Atlantic are perturbed by ±2 K, both individually and in combination. The WRF simulations are additionally configured with wind and pressure nudging over the full simulation period and without nudging during the event itself, allowing thermodynamic and dynamical effects to be better separated. To diagnose the origin and transport pathways of moisture feeding the event, we use FLEXPART-WRF in backward trajectory mode, driven by WRF output, together with a moisture source diagnostic. Air parcels arriving in the Central European target region are traced backward for up to ten days in order to identify the dominant moisture source regions contributing to the precipitation.The analysis identifies eastern European land areas and the Mediterranean as the primary moisture source regions for Storm Boris, while the Atlantic and Black Sea provide smaller but still relevant contributions. Among the surrounding ocean basins, the Mediterranean is the dominant marine source in all experiments. The sensitivity experiments show that cooling one basin generally reduces its direct moisture contribution, but that this loss is often partly compensated by enhanced moisture uptake from another basin, indicating a redistribution of moisture sources rather than a simple overall reduction. In contrast, warming increases the overall oceanic contribution and is associated with higher precipitation. Overall, the results indicate an average precipitation increase of about 3% per kelvin of SST warming for this event, highlighting the contribution of climate-driven SST increases to the exceptional rainfall observed during Storm Boris.

Avoiding desiccation is paramount for all terrestrial insects, especially in climatically challenging mountainous environments characterised by rapid changes in temperature. A key component in the insects' repertoire to avoid water loss is a thin waxy layer on the insects' cuticle - the cuticular hydrocarbons (CHCs). These can be modified to reduce cuticular transpiration, but they are also restricted by their communication function and by physiological properties like age. Here, we use montane bumblebees to assess the relative importance of abiotic features (temperature) and internal factors (relatedness and age) in 'shaping' the cuticular hydrocarbon profiles. We perform inter- and intraspecific comparisons of twelve species collected along a 1560 m elevational climate gradient (from 800 to 2400 m a.s.l.). Intraspecific analyses revealed that the cuticular hydrocarbon profiles are associated with bumblebee worker age. Interspecific comparisons showed that cuticular hydrocarbon profiles are species-specific corroborating the genetic element. Our results suggest that the cuticular hydrocarbon profile responses to abiotic factors - like temperature and humidity - are constrained by physiological features. This highlights the intricacy of different features shaping the CHC profiles and raises issues about the acclimatisation capability of these important pollinators to climate changes. We suggest that future investigations of cuticular hydrocarbon profiles should incorporate physiological features that are related to fitness, such as the insects' nutrition, body size and fat content.

Renewable and electrified energy systems are highly weather-dependent, making them vulnerable to climate change. Energy system modeling therefore requires high-quality data that captures the spatiotemporal complexity of climate conditions. We present SECURES-Energy, an open-access dataset providing hourly electricity demand and supply data for Europe at the national level from 1981 to 2100. Historical data are derived from ERA5 reanalysis, while future projections use two EURO-CORDEX scenarios (RCP 4.5/RCP 8.5). The dataset includes onshore and offshore wind, solar photovoltaic (PV), and hydropower generation, as well as all electricity demand components such as heating, cooling, and mobility. Results indicate no consistent trends for solar PV and hydropower across Europe. Offshore wind declines by up to -4%/-3% by 2035-2064 and -6%/-9% by 2071-2100 relative to 1981-2010. Cooling demand rises sharply (up to +80%/+149% by mid-century; +129%/+317% by end-century), while heating demand falls (-19%/-24% by mid-century; -25%/-40% by end-century). These findings highlight substantial climate-driven shifts in future electricity demand and supply.

Stationary precipitation measurements are frequently affected by undercatch errors, which are particularly pronounced in cold and alpine regions with strong winds. Since gridded precipitation products used in land surface modelling are often derived from spatial interpolation of meteorological station data, these measurement errors propagate directly into gridded datasets. Hydrological models provide a powerful tool for validating precipitation products through their integration of multiple water balance components. In this study, we develop a monthly undercatch correction product for Austria using Generalized Additive Models (GAMs) trained on station observations with geographical exposure and terrain elevation as predictors (R² > 0.76 in cross-validation), and apply these corrections to existing gridded precipitation datasets.We validate the undercatch correction using the conceptual rainfall-runoff model COSERO across Austria and in two high-alpine reservoir catchments (Kölnbrein and Schlegeis). Austrian-wide simulations demonstrate elevation-dependent improvements, with reduced runoff biases particularly in catchments above 1500-2000 m elevation. In the alpine case study regions, the corrected precipitation closes the long-term water balance where uncorrected data showed deficits exceeding 20 %. The physically-based snowpack model Alpine3D, validated against stereo-satellite observations, shows substantial improvements in snow depth simulations with median biases decreasing from -0.87 m to +0.15 m. Additionally, the correction improves representation of snow melt-out behaviour during the ablation season and enables more realistic simulation of long-term glacier volume changes. These results highlight the importance of accounting for undercatch errors in high-alpine terrain and demonstrate the value of comprehensive hydrological validation for precipitation products.Acknowledgements: We thank the Austrian Climate Research Programme (ACRP), and the Verbund Energy4Business GmbH for funding, fruitful discussions and providing us with data.

Aim: To quantify how interacting regional anthropogenic pressures and climate warming have driven long-term changes in wild bee community composition and diversity. Location: Eight sites within a 300 km(2) region around Linz, Upper Austria (266-616 m a.s.l.). Methods: We analysed 11,934 museum and contemporary occurrences (out of a larger dataset of 17,515 records) of 340 wild bee species collected between 1910 and 2021. Temporal presence-absence data (in six 15-year intervals) were linked to interval-averaged temperature, precipitation and human population density (proxy for anthropogenic impact) as well as sampling effort (total number of sampling events). We fitted a single binomial generalised linear mixed model (GLMM) with species and site as random effects and species category (Intermittent, Disappearing, Appearing) interacting with environmental predictors. Post hoc trend comparisons and trait-based analyses further elucidated group-specific responses. Results: Community turnover drove an similar to 18% decline in species richness over the observation period. Disappearing specialists declined under rising human impact and warming. Intermittent species were favoured by wetter and warmer intervals but negatively affected by higher human densities. Appearing generalists expanded under warming and anthropogenic pressures with no precipitation response. Omnipresent generalists remained consistently present irrespective of environmental variation, underscoring their broad tolerance but contributing to functional homogenisation. Sampling effort improved detection but did not alter these ecological patterns. Main Conclusions: Compounded anthropogenic pressures and climate warming drive specialist losses and generalist expansions, while precipitation buffers only intermittent species. Omnipresent generalists sustain baseline pollination, but community shifts toward generalists homogenise functional diversity and undermine ecosystem-level pollination resilience. Conservation must address multiple drivers-protecting specialist habitats, maintaining connectivity as well as climate- and precipitation-sensitive refugia-to preserve functional diversity and pollination services under ongoing global change.

Austria is experiencing increasingly frequent and prolonged drought periods as well as a rising number of heat days, both of which adversely affect agricultural productivity. The magnitude of these impacts depends on crop-specific growing periods and stress tolerances. Here, we assess how projected climate conditions during 1990-2039, assuming the shared socio-economic pathway SSP3-7.0 for the future period, influence yield expectations for winter wheat, spring barley, soybean, maize, potatoes, and grassland in Austria.Meteorological forcing is derived from the high-resolution General Circulation Model "Climate Change Adaptation Digital Twin", developed by the European Centre for Medium-Range Weather Forecasts. The data are statistically downscaled to a spatial resolution of 250 m and daily temporal resolution using Quantile Delta Mapping, with an observation-based in-house reference dataset for the historical period 1990-2019. Crop phenology, soil water balance, and combined heat and drought stress are simulated using the Agricultural Risk Information System. Phenological stage entry dates are computed from accumulated excess temperatures calibrated against near-surface air temperature observations and satellite-based remote sensing data for the years 2020, 2021, and 2023.The projections indicate increasing levels of crop stress accompanied by enhanced interannual variability. Winter wheat is least affected by combined heat and drought stress due to its relatively early maturity. However, drought and heat extremes lead to substantial yield reductions across all modeled crops in approximately half of the projected years. Overall, potential benefits of warmer temperatures during early growing stages are outweighed by increasing heat and drought stress later in the season.

The main objective of the Austria Fire Futures study is to develop a unique and innovative framework for fire risk assessment by producing high-resolution fire risk hotspot maps under multiple climate change scenarios. These maps integrate novel insights on local fuel types into forest and wildfire risk models, including mountain-specific variables such as topography, morphology, and recreational activities.To generate fire risk information at the local scale, advanced fire hazard modeling is required to identify vulnerable forest types in combination with topographic effects. Recent wildfire events in the Austrian Alps have demonstrated that social factors—particularly hiking tourism—are currently underrepresented in fire risk assessments. In response, this study aims to advance fire risk hotspot mapping as a foundational element for forest and wildfire prevention. Such mapping is essential for integrated fire management, encompassing prevention, suppression, and post-fire measures, while contributing to climate change mitigation and minimizing impacts on ecosystems, ecosystem services, and human well-being.We present modeling results from the Wildfire Climate Impacts and Adaptation Model (FLAM), a process-based fire risk model operating at a daily time step. FLAM employs machine learning techniques to calibrate extended suppression efficiency based on spatial segmentation of landscapes. Historical ground data on burned areas in Austria were used for model calibration and validation. The results include historical simulations (2001–2020) and future projections (2021–2100) of burned area across Austria at 1 km spatial resolution, based on an ensemble of downscaled climate change scenarios. In addition, FLAM was applied to Lower Austria at 250 m resolution, using the most recent high-resolution datasets on fuels, forest cover, human ignition probability, and response times.The results improve our understanding of fire-vulnerable forest areas in the Alpine region and how these vulnerabilities may shift over time and space under changing climate and fuel conditions. This knowledge enables experts, practitioners, and the broader public to explore plausible future fire regimes and to derive robust short-, medium-, and long-term recommendations for fire-resilient and sustainable forest management, as well as for wildfire preparedness and emergency planning.

Models like the conceptual hydrological model COSERO and the physically-based mountain surface process model Alpine3D are highly sensitive to meteorological inputs, especially precipitation. Gridded precipitation data sets usually originate from spatially interpolated weather station data, which are not corrected for precipitation undercatch. The term precipitation undercatch describes the deviation of measured precipitation in rain gauges to the actual amount in a given area due to several factors like instrument design or effects of splash, evaporation and especially wind. Specifically solid precipitation is prone to wind drag. Because of these effects, models or model chains fail to simulate observations for discharge, reservoir inflow, snow and ice melt as well as glacier mass balance due to the lack of realistic precipitation input into the system in high-alpine regions. However, correcting the undercatch directly within the gridded data set leads to an overestimation of precipitation, which has two main reasons: First, undercatch correction functions are not derived for alpine temperatures and wind speeds. Second, stations at lower elevations, where the undercatch is comparatively small, are usually over-represented in gridded data sets. Therefore, we composed a method to perform a precipitation undercatch correction in high-alpine areas by using a gridded precipitation data set and quality-controlled, representative station data in the vicinity of snow-dominated and glacierized catchments as well as their altitude and exposure to generate spatial undercatch correction fields for three selected catchments in Austria (Maltatal, Zillertal and Vernagtferner) on a monthly basis. These correction factors are a function of elevation and the month and result from a stepwise linear interpolation with elevation, whereas the highest factors are obtained in the winter months due to low temperatures. Using the topography and averaging over whole catchments, the highest (lowest) correction factors are obtained in February (August), ranging from 2.16 to 1.04, depending on the catchment and season. The meteorological data (with and without the undercatch corrected precipitation) was used as an input for a coupled snow-glacier-discharge simulation with the models COSERO and Alpine3D on the selected catchments. The output was validated against reservoir inflow, observed glacier mass balances and satellite derived snow depth maps. With the undercatch corrected precipitation, the models perform substantially better in simulating observations for glacier mass balance as well as reservoir inflow. Acknowledgements: We thank VERBUND AG for fruitful discussions and providing us with data.

Rockfalls and rockslides are a common hazard in alpine terrain and are major factor of alpine landscape evolution. They are characterized by a complex combination of geological, hydrological, geomechanical and meteorolocical processes and occur in a wide variety of geological and structural settings and in response to various loading and triggering processes. In the Alps in particular, extremely rapid rock avalanches reaching a volume of several 10000 m3 or more have the potential to cause serious damage to both humans and infrastructure. As global warming progresses, the meteorological and climatological factors that influence rock avalanche formation will change. Especially, in the high mountain environment rock avalanches are strongly influenced by climate change due to thawing of permafrost and the retreat of glaciers. Less obvious is the influence of climate change on the formation of rock avalanches at lower altitudes, and thus there is a need for additional research. In this study, we investigate the impact of global warming on selected rock avalanche case studies with volumes above several tens of thousands of cubic meters. The study area covers approx. 3400 km2 in the metamorphic rock mass of the Ötztal Stubai Crystalline, the Silvretta and the Glockner Nappes as well as the units of the Engadin Window of the Tyrolian Alps, Austria. The aim of this work is to identify the processes that led to our case studies and if these processes are influenced by climate change factors, such as changes in temperature, precipitation, freeze-thaw cycles, snow coverage, etc. The climatic factors will be investigated in terms of both their short-term and long-term influence on the trigger mechanisms. Advanced remote sensing techniques were used on site to carry out small to large-scale investigations. Terrestrial laser scanning (TLS) and Airborne laser scanning (ALS) enables us to create high-resolution recordings of inaccessible rock faces, supported by 3D point cloud analyzing tools. In addition, where TLS campaigns are not possible, we use an unmanned aerial vehicle (UAV) photogrammetry system that provides 3D point clouds and delivers a 3D model of the site. Geological field investigations were performed to record lithological, hydrogeological and structural features. This results in a comprehensive geological model of the failure area. A 3D discontinuity network was developed based on the combined analyses of remote sensing and discontinuity mapping data, providing the basis for structural geological analyses and distinct element modelling studies. With regard to the above criteria, we have selected several case studies. Most of the case studies are located well above 2500 m above sea level in glaciated or recently glaciated areas. For all case studies, we were able to document at least one rock avalanche event with a volume exceeding several 10000 m3. A high-resolution climate model was created for the documented events. We then began to collect and evaluate the existing literature on the individual case studies.

Rainfall is the main trigger for debris flows in torrent catchments. A systematic analysis of the critical rainfall for debris-flow initiation in Austria is lacking. This study quantifies and compares rainfall events that historically triggered debris flows and fluvial floods in Austria and assesses the expected impact of climate change on the frequency and spatial extent of such events. We derive critical rainfall conditions using hourly rainfall estimates based on radar-rain gauge data for more than 3600 torrent events between 2003 and 2022. Past and future return periods are estimated based on an ensemble of regional climate projections covering three emission scenarios until 2100. We find that the regional variations of the critical rainfall patterns are greater than the differences between the types of processes. A general dependence of triggering rainfall on the antecedent rainfall cannot be confirmed. In a warming climate, both the probability of occurrence and the affected area in Austria are projected to increase for both process types and across all emission scenarios. However, these changes are unevenly distributed throughout Austria. The results indicate that regional-scale predictions can improve mountain risk management in a changing climate, while risk management for individual torrents will benefit from process-based understanding.

Wildfires have become an increasingly frequent occurrence in Central Europe due to changing climate patterns, prolonged dry spells, and rising temperatures. Effective wildfire management relies on knowledge of fire behaviour, which enhances the need for reliable fire propagation models. However, modelling wildfire behaviour in Central Europe presents unique challenges, particularly when adapting models originally developed for North America and the Mediterranean. Differences in vegetation types, climate conditions, and topography complicate the direct application of these models. Moreover, small-scale wind variations, which play a critical role in fire spread, are often inadequately captured by existing models, leading to reduced predictive accuracy.One of the primary hurdles in wildfire modelling for any region in Central Europe is the lack of high-resolution fuel data, that capture the fire behaviour of the main vegetation types and are therefore crucial for accurate fire spread prediction. Additionally, there is a scarcity of well-documented fire events that can provide reliable information for model calibration and validation. Without data on the burned area per time unit, the intensity and duration of the fire event, it is difficult to adapt models to regional conditions or assess their reliability in real wildfire scenarios.This study explores the performance of various wildfire behaviour models applied to a case study in Austria. By comparing the outputs of different models, insights were gained about their suitability for predicting fire behaviour under Central European conditions. Lessons learnt from this study highlight the need for region-specific adaptations on the fuel data, improvements in data availability, and more robust modelling approaches that can account for localized wind effects and fuel variability. These findings contribute to advancing wildfire prediction capabilities in Central Europe, supporting better-informed fire management strategies.