[199] Winter, G., McCaffrey, S., Vogt, C. A., 2009. **The role of community policies in defensible space compliance. Forest Policy and Economics** 11 (8), 570-578. https://doi.org/10.1016/j.forpol.2009.07.004, INRMM-MiD:5734088. (Cited 2 times on page 35)

[200] Tedim, F., Leone, V., Xanthopoulos, G., 2016. **A wildfire risk management concept based on a social-ecological approach in the European Union: Fire Smart Territory. International Journal of Disaster Risk Reduction** 18, 138-153. https://doi.org/10.1016/j.ijdrr.2016.06.005, INRMM-MiD:14485293. (Cited on page 35)

[201] Pausas, J. G., Fernández-Muñoz, S., 2012. **Fire regime changes in the Western Mediterranean Basin: from fuel-limited to drought-driven fire regime. Climatic Change** 110 (1-2), 215-226. https://doi.org/10.1007/s10584-011-0060-6, INRMM-MiD:9112219. (Cited 2 times on page 35)

[202] Nunes, A. N., Lourenço, L., Meira, A. C. C., 2016. **Exploring spatial patterns and drivers of forest fires in Portugal (1980-2014). Science of The Total Environment** 573, 1190-1202. https://doi.org/10.1016/j.scitotenv.2016.03.121, INRMM-MiD:14429360. (Cited 2 times on page 35)

[203] San-Miguel-Ayanz, J., Chuvieco, E., Handmer, J., Moffat, A., Montiel-Molina, C., Sandahl, L., Viegas, D., 2017. **Climatological risk: wildfires.** In: Poljanšek, K., Marín Ferrer, M., De Groeve, T., Clark, I. (Eds.), *Science for disaster risk management 2017: knowing better and losing less*. Vol. 28034 of EUR - Scientific and Technical Research. Publications Office of the European Union, pp. 294-305. http://drmkc.jrc.ec.europa.eu/portals/0/Knowledge/ScienceforDRM/ch03_s03/ch03_s03_subch0310.pdf, INRMM-MiD:14445352. (Cited on page 35)

[204] Ager, A. A., Vaillant, N. M., Finney, M. A., 2010. **A comparison of landscape fuel treatment strategies to mitigate wildland fire risk in the urban interface and preserve old forest structure. Forest Ecology and Management** 259 (8), 1556-1570. https://doi.org/10.1016/j.foreco.2010.01.032, INRMM-MiD:6862982. (Cited on page 35)

[205] Graham, R. T., Harvey, A. E., Jain, T. B., Tonn, J. R., 1999. **The effects of thinning and similar stand treatments on fire behavior in Western forests. Vol. 463 of General Technical Report (GTR).** Pacific Northwest Research Station, U.S. Department of Agriculture, Forest Service. 36 pp. https://doi.org/10.2737/PNW-GTR-463, INRMM-MiD:14427413. (Cited on page 35)

[206] Fernandes, P. M., Vega, J. A., Jiménez, E., Rigolot, E., 2008. **Fire resistance of European pines. Forest Ecology and Management** 256 (3), 246-255. https://doi.org/10.1016/j.foreco.2008.04.032, INRMM-MiD:13796018. (Cited on page 35)

[207] Della Rocca, G., Hernando, C., Madrigal, J., Danti, R., Moya, J., Guijarro, M., Pecchioli, A., Moya, B., 2015. **Possible land management uses of common cypress to reduce wildfire initiation risk: a laboratory study. Journal of Environmental Management** 159, 68-77. https://doi.org/10.1016/j.jenvman.2015.05.020, INRMM-MiD:13745883. (Cited on page 35)

[208] Camia, A., Libertà, G., San-Miguel-Ayanz, J., 2017. **Modeling the impacts of climate change on forest fire danger in Europe: sectorial results of the PESETA II Project. Publications Office of the European Union, Luxembourg. ISBN:978-92-79-66259-1**, https://doi.org/10.2760/768481, INRMM-MiD:14314418. (Cited 8 times on pages 36, 37, 38, and 39)

[209] Amatulli, G., Camia, A., San-Miguel-Ayanz, J., 2013. **Estimating future burned areas under changing climate in the EU-Mediterranean countries. Science of The Total Environment** 450-451, 209-222. https://doi.org/10.1016/j.scitotenv.2013.02.014, INRMM-MiD:13096535. (Cited 4 times on pages 36, 37, and 39)

[210] Sciortino, I., Ganci, A., 2005. **Cause dolose e colpose degli incendi in Sicilia: proposte per la rimozione degli interessi e dei bisogni alla base del fenomeno. In: Atti del convegno: incendi boschivi e rurali in Sardegna - Dall'analisi delle cause alle proposte d'intervento. Regione Autonoma della Sardegna - Corpo Forestale e di Vigilanza Ambientale, pp. 177-185. http://www.regione.sardegna.it/documenti/1_39_20050927125556.pdf,** INRMM-MiD:14388312. (Cited on page 36)

[211] Regione Autonoma della Sardegna, 2013. **Piano regionale di previsione, prevenzione e lotta attiva contro gli incendi boschivi 2011-2013 - Revisione 2013. Regione Autonoma della Sardegna, Sardegna, Italy. http://www.regione.sardegna.it/documenti/1_73_20130618125303.pdf,** INRMM-MiD:14389879. (Cited on page 36)
[212] Lozano, O. M., Salis, M., Ager, A. A., Alcasena Urdiroz, F., Arca, B., Finney, M. A., Bacciu, V., Spano, D., 2015. **Spatiotemporal variations in wildfire regime and exposure for Sardinia, Italy**. In: Atti del Secondo Congresso Internazionale di Selvicoltura = Proceedings of the Second International Congress of Silviculture. Accademia Italiana di Scienze Forestali, pp. 975-981. https://doi.org/10.4129/2cis-ol-var, INRMM-MiD:14388027. (Cited on page 36)

[213] Turco, M., Bedia, J., Di Liberto, F., Fiorucci, P., von Hardenberg, J., Koutsias, N., Llasat, M.-C., Xystrakis, F., Provenzale, A., 2016. **Decreasing fires in Mediterranean Europe. PLoS ONE 11** (3), e0150663+. https://doi.org/10.1371/journal.pone.0150663, INRMM-MiD:14143684. (Cited on page 37)

[214] Sousa, P. M., Trigo, R. M., Pereira, M. G., Bedia, J., Gutiérrez, J. M., 2015. **Different approaches to model future burnt area in the Iberian Peninsula. Agricultural and Forest Meteorology** 202, 11-25. https://doi.org/10.1016/j.agrformet.2014.11.018, INRMM-MiD:14390762. (Cited on page 37)

[215] Steele, B., 2000. **Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sensing of Environment** 74 (3), 545-556. https://doi.org/10.1016/s0034-4257(00)00145-0, INRMM-MiD:14389795. (Cited on page 38)

[216] Sun, Q., Pfahringer, B., 2011. **Bagging ensemble selection**. In: Wang, D., Reynolds, M. (Eds.), AI 2011: Advances in Artificial Intelligence. Vol. 7106 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 251-260. https://doi.org/10.1007/978-3-642-25832-9_26, INRMM-MiD:14389367. (Cited on page 38)

[217] Bischl, B., Mersmann, O., Trautmann, H., Weihs, C., 2012. **Resampling methods for meta-model validation with recommendations for evolutionary computation. Evolutionary Computation** 20 (2), 249-275. https://doi.org/10.1162/evco_a_00069, INRMM-MiD:12313057. (Cited on page 38)

[218] Sun, Q., Pfahringer, B., 2012. **Bagging ensemble selection for regression**. In: Thielscher, M., Zhang, D. (Eds.), AI 2012: Advances in Artificial Intelligence. Vol. 7691 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 695-706. https://doi.org/10.1007/978-3-642-35101-3_59, INRMM-MiD:12350921. (Cited on page 38)

[219] Tantithamthavorn, C., McIntosh, S., Hassan, A. E., Matsumoto, K., 2017. **An empirical comparison of model validation techniques for defect prediction models. IEEE Transactions on Software Engineering** 43 (1), 1-18. https://doi.org/10.1109/tse.2016.2584050, INRMM-MiD:14389824. (Cited on page 38)

[220] Xystrakis, F., Kallimanis, A. S., Dimopoulos, P., Halley, J. M., Koutsias, N., 2014. **Precipitation dominates fire occurrence in Greece (1900–2010): its dual role in fuel build-up and dryness. Natural Hazards and Earth System Science** 14 (1), 21-32. https://doi.org/10.5194/nhess-14-21-2014, INRMM-MiD:14390604. (Cited on page 38)

[221] Oehler, F., Oliveira, S., Barredo, J. I., Camia, A., San-Miguel-Ayanz, J., Pettenella, D., Mavsar, R., 2012. **Assessing European wild fire vulnerability. Geophysical Research Abstracts** 14, 9452+. INRMM-MiD:14377675. (Cited 2 times on pages 38 and 39)

[222] Harrower, M., Brewer, C. A., 2003. **ColorBrewer.org: an online tool for selecting colour schemes for maps. The Cartographic Journal** 40 (1), 27-37. https://doi.org/10.1179/000870403235002042, INRMM-MiD:9026683. (Cited on page 39)

[223] Ruffault, J., Martin-StPaul, N., Duffet, C., Goge, F., Mouillot, F., 2014. **Projecting future drought in Mediterranean forests: bias correction of climate models matters! Theoretical and Applied Climatology** 117 (1-2), 113-122. https://doi.org/10.1007/s00704-013-0992-z, INRMM-MiD:14390746. (Cited on page 38)

[224] Bedia, J., Herrera, S., San Martín, D., Koutsias, N., Gutiérrez, J. M., 2013. **Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling. Climatic Change** 120 (1-2), 229-247. https://doi.org/10.1007/s10584-013-0787-3, INRMM-MiD:14390755. (Cited on page 38)

[225] Taylor, K. E., Stouffer, R. J., Meehl, G. A., 2011. **An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society** 93 (4), 485-498. https://doi.org/10.1175/bams-d-11-00094.1, INRMM-MiD:10490392. (Cited on page xix)

[226] San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), 2016. **European atlas of forest tree species. Publications Office of the European Union, Luxembourg. ISBN:978-92-79-36740-3, https://w3id.org/mtv/FISE-Comm/v01, INRMM-MiD:13984530.** (Cited 3 times on pages xix, xx, and xxi)
[227] Giorgi, F., Jones, C., Asrar, G. R., 2009. **Addressing climate information needs at the regional level: the CORDEX framework. World Meteorological Organization Bulletin 58** (3), 175-183. https://public.wmo.int/en/node/6138, INRMM-MiD:14430520. (Cited on page xix)

[228] Planton, S., 2013. **Annex III: glossary**. In: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P. M. (Eds.), *Climate Change 2013: the physical science basis - Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change*. Cambridge University Press, pp. 1447-1465. http://www.ipcc.ch/report/ar5/wg1/, INRMM-MiD:14290450. (Cited 2 times on page xxi)
List of abbreviations and definitions

<table><tr><td>Anomaly</td><td>in climate-change analysis, it refers to the difference between the characteristics of a given quantity over two time periods. Given a statistic to aggregate the quantity time-series over a time period, the anomaly is the difference between the statistic over a period to investigate (e.g. future) and the same statistic in a control period.</td></tr><tr><td>BUI</td><td>Buildup Index, a component of the FWI system. It combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire. See Figure 6.</td></tr><tr><td>CMIP5</td><td>Coupled Model Intercomparison Project phase 5, a set of coordinated climate model experiments, dealing with global coupled ocean-atmosphere general circulation models (GCMs) [225]. See http://cmip-pcmdi.llnl.gov/cmip5/.</td></tr><tr><td>Computational model</td><td>mathematical model in computational science requiring computational resources to analyse or estimate specific statistics and information on the behaviour of a natural or artificial system. Definition from San-Miguel-Ayanz et al. [226].</td></tr><tr><td>CORDEX</td><td>Coordinated Regional Climate Downscaling Experiment initiative, a coordinate effort to advance and the science and application of regional climate downscaling [227]. See http://www.cordex.org.</td></tr><tr><td>DC</td><td>Drought Code, a component of the FWI system. It models a standard moisture content of deeper, compact, organic layers. This D-TM is able to track seasonal drought effects on forest fuels. See Figure 6 and Table 1.</td></tr><tr><td>DMC</td><td>Duff Moisture Code, a component of the FWI system. It models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). See Figure 6 and Table 1.</td></tr><tr><td>D-TM</td><td>Data-transformation models or modules. In computational science, the architecture of models may be structured in a data-oriented modular way. A D-TM is a conceptual modelling-unit which transforms a set of input data and model parameters into a corresponding set of output data [66, 67, 68]. In this context, "data" as a concept is extended to include not only physical measurements but also derivative data (typically, derived as output of one or more models) and, as a particular case, the value of model parameters. D-TMs may be composed of sub-units - which are D-TMs themselves. Therefore, a D-TM may be described as a chain of D-TM units which exchange a flow of data, from the initial inputs up to the final desired output values. Data can also be exchanged asynchronously between D-TMs which physically run in different computational facilities. This eases the integration of the various conceptual modelling-units even when they are implemented in different programming languages, and eases the interaction among multiple research teams.</td></tr><tr><td>EFFIS</td><td>European Forest Fires Information System. It consists of a modular web geographic information system that provides near real-time and historical information on forest fires and forest fire regimes in the European, Middle Eastern and North African regions. Fire monitoring in EFFIS comprises the full fire cycle, providing information on the pre-fire conditions and assessing post-fire damages. See http://effis.jrc.ec.europa.eu/.</td></tr><tr><td>EURO-CORDEX</td><td>European branch of the CORDEX initiative. EURO-CORDEX is a multi-institution voluntary effort to produce ensemble climate simulations for the European continent, using multiple downscaling models (regional climate models, RCP) to improve global circulation models (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) [105, 92, 106, 107]. See http://www.euro-cordex.net.</td></tr><tr><td>FFMC</td><td>Fine Fuel Moisture Code, a component of the FWI system. It provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel. See Figure 6 and Table 1.</td></tr><tr><td>FWI</td><td>Fire Weather Index, a component of the FWI system. As a numerical index (not to be confused with the FWI-system of indices), it offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI. See Figure 6.</td></tr></table>
<table>
   <tr>
    <td>FWI-system</td>
    <td>The Canadian Forest Fire Weather Index system, an index of fire danger to account for the effects of weather on forest fuel and fire. The FWI is designed to provide a uniform numerical rating of the relative fire potential, by dynamically combining the information from local temperature, wind speed, relative humidity, and precipitation (24-hour rainfall) values. Provided a daily time series for each of these weather data variables is available, the system is able to process either actual observations or future simulated estimates. The FWI system is standardised to consider the behaviour of a reference fuel type (mature pine stand), irrespective of other factors affecting fire danger such as the topography and the actual or future fuel details [16, 59]. Among the various indices composing the FWI-system, a specific component of special importance is the FWI numerical index, which aggregates the other indices. See Figure 6.</td>
   </tr>
   <tr>
    <td>GCM</td>
    <td>Global circulation model, or global climate model. It is a climate model able to approximate the general circulation of atmosphere (and/or of oceans) at the global scale, considering the main fluxes of mass and energy. As a trade-off for its ability to cover the global scale, its spatial resolution is typically lower compared with RCMs – which may be exploited to refine the details of GCM simulations for a particular region of interest.</td>
   </tr>
   <tr>
    <td>Geospatial</td>
    <td>in computational science, it refers to data or information which is geographically distributed and covers significantly broad spatial extents. Under these circumstances, for example the simple approximation of the portion of Earth's surface covered by the spatial extent as a geometrical plane is no more valid. Definition from San-Miguel-Ayanz et al. [226].</td>
   </tr>
   <tr>
    <td>GeoSemAP</td>
    <td>Geospatial Semantic Array Programming. Geospatial application of the SemAP paradigm, where the conceptual units (D-TMs) of the modelling workflow are a composition of geospatial transformations and array-based D-TMs [67, 68].</td>
   </tr>
   <tr>
    <td>GHG</td>
    <td>atmospheric greenhouse gas.</td>
   </tr>
   <tr>
    <td>GO-ESSP</td>
    <td>Global Organization for Earth System Science Portals. See http://go-essp.gfdl.noaa.gov/.</td>
   </tr>
   <tr>
    <td>HS</td>
    <td>Habitat suitability: potential suitability for a certain organism (e.g. a tree species) to live in a given local habitat. Although there is no agreement in defining habitat within the ecological literature, a working definition for operational purposes has been proposed as "description of a physical place, at a particular scale of space and time, where an organism either actually or potentially lives" [103]. As a quantity, HS is generally varying from 0 (0%, unsuitable habitat) to 1 (100%, potentially highly suitable habitat). For an overview on terminology, ambiguity and the multifaceted concepts related to HS, see e.g. de Rigo et al. [47].</td>
   </tr>
   <tr>
    <td>IPCC</td>
    <td>Intergovernmental Panel on Climate Change. See http://www.ipcc.ch/.</td>
   </tr>
   <tr>
    <td>ISI</td>
    <td>Initial Spread Index, a component of the FWI system. It represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of variable quantities of fuel. See Figure 6.</td>
   </tr>
   <tr>
    <td>JRC</td>
    <td>Joint Research Centre of the European Commission. See https://ec.europa.eu/jrc/en/about/jrc-in-brief.</td>
   </tr>
   <tr>
    <td>P</td>
    <td>Precipitation. One of the input variables required by the FWI system. See the Section on modelling structure and semantics.</td>
   </tr>
   <tr>
    <td>PESETA</td>
    <td>The context behind this study is based on a series of projects mostly developed within the European Commission, Joint Research Centre (JRC). Within this project series, PESETA (Projection of Economic impacts of climate change in Sectors of the European Union based on bottom-up Analysis, https://ec.europa.eu/jrc/en/peseta), cross-disciplinary aspects are essential.</td>
   </tr>
   <tr>
    <td>PESETA III</td>
    <td>The third instance of PESETA (PESETA III) focuses on supporting the implementation of Action 4 of the EU Adaptation Strategy by deepening and further refining existing JRC bottom-up analyses of climate change impacts. It contributes to report on the Strategy's implementation that the Commission will present to the European Council and Parliament. A common set of five climate scenario realisations (model runs) drive the assessment of sectoral biophysical impact models with a strategic focus on the biophysical dimension of impacts. The analysis includes the 2030s time horizon, and explores the challenging characterisation of extreme events with their peculiar uncertainty, and aims at fostering an updated review of potential adaptation options.</td>
   </tr>
  </table>
<table><tr><td>RCP</td><td>Representative Concentration Pathways. RCPs are referred to as pathways in order to emphasize that their primary purpose is to provide time dependent projections of atmospheric greenhouse gas (GHG) concentrations [9, 228].</td></tr><tr><td>RCP8.5</td><td>High emission RCP scenario of climate change. It is derived after the A2r scenario providing a revised quantification of the original IPCC scenario family SRES A2 [75, 109] and assumes an increase of the radiative forcing throughout the 21st century up to reach an approximate level of 8.5 W per m² by the end of the century.</td></tr><tr><td>RCM</td><td>Regional climate model. It is a climate model typically having a higher spatial resolution compared with the one of GCMs. As a trade-off, its spatial extent is limited to a particular region of the globe. It is used to refine the details of GCM simulations for a particular region.</td></tr><tr><td>RH</td><td>Relative humidity. One of the input variables required by the FWI system. See the Section on modelling structure and semantics.</td></tr><tr><td>scen</td><td>Climate scenario instance. It is defined as either an observed (i.e. historical data) or estimated time series - e.g. under climate change - which corresponds to a particular realisation of the variables. See the Section on modelling structure and semantics.</td></tr><tr><td>Scenarios</td><td>The future evolution of greenhouse gas (GHG) emissions is highly uncertain. Scenarios are alternative plausible descriptions of how the future may unfold. Each scenario is based on a coherent set of assumptions concerning key driving forces (e.g. demographic and socio-economic development, rate of technological change, prices) and relationships. Neither predictions nor forecasts, scenarios are tools to support the analysis on how driving forces may influence the dynamics of future emissions. They are useful to assess the implications of development, potential impacts, adaptation and mitigation actions, and the associated uncertainties [109, 228].</td></tr><tr><td>Semantic constraint</td><td>in computational modelling, it formally expresses a logical or mathematical property which characterises the quantitative meaning (semantics) of a certain quantity [78, 72, 73, 68, 226]. For example, considering the annual time series of the weather-driven fire danger in a given area, its 90% quantile logically must be greater than or equal to the median value in the same area, while this constraint does not hold for the anomaly of the 90% quantile compared to the anomaly of median values.</td></tr><tr><td>SemAP</td><td>Semantic Array Programming. In computational science, a computational modelling approach to compactly process arrays of data preserving the consistency of their underpinning semantics [72, 73, 67, 68]. SemAP is based on the modularisation of the modelling workflow into conceptual units (modules) of data-transformation (see D-TM), and on the systematic use of array-based semantic constraints. In this work, SemAP is applied for the statistical analysis of the FWI system.</td></tr><tr><td>SRES</td><td>IPCC Special Report on Emissions Scenarios [109].</td></tr><tr><td>SRES A2</td><td>The SRES A2 storyline and scenario family "describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in high population growth. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other storylines" [109].</td></tr><tr><td>T</td><td>Temperature. One of the input variables required by the FWI system. See the Section on modelling structure and semantics.</td></tr><tr><td>w</td><td>Wind speed. One of the input variables required by the FWI system. See the Section on modelling structure and semantics.</td></tr><tr><td>WUI</td><td>Wildland-Urban Interface. Agriculture abandonment may increase the available fuel in areas that become wildland. Urban expansion may generate new settlements surrounded by wildland. In both cases, these transitional areas between unoccupied land and human settlements may be particularly exposed to wildfire impacts. See the Section on human factors and fire.</td></tr></table>

See also Table 2, Table 3, and Table 4.
List of figures

<table><tr><td>Figure 1 Annual burnt area of five EU-Med countries and of 12 other countries. Total annual hectares burnt for the EU-Med and the other countries, and percentage of burnt area per year</td><td>1</td></tr><tr><td>Figure 2 Seasonal drought effects on fuel moisture of the deeper layer of more compact organic matter in the ground, as estimated by a specific component of the Canadian Forest Fire Weather Index system in present, and under two climate change scenario</td><td>5</td></tr><tr><td>Figure 3 Overall weather-driven forest fire danger in present, and under two climate change scenarios, according to two different climate models selected to demonstrate the effect of using different climate models</td><td>5</td></tr><tr><td>Figure 4 Annual burnt area (hectares) of five EU-Med countries and of 12 other countries; total annual hectares burnt for each of the EU-Med countries; total annual hectares burnt for each of the other countries</td><td>9</td></tr><tr><td>Figure 5 Annual burnt area (relative proportion) of five EU-Med countries and of 12 other countries; relative proportion with the aggregated total of the EU-Med countries of the annual burnt area of each EU-Med country; relative proportion with the aggregated total of the non EU-Med countries of the annual burnt area of each non EU-Med country</td><td>10</td></tr><tr><td>Figure 6 Modelling architecture for the application of the Canadian Forest Fire Weather Index System to assess the climatic signal of fire danger potential in Europe</td><td>14</td></tr><tr><td>Figure 7 Average over 9 EURO-CORDEX regional model simulations for the RCP8.5 scenario. Estimated behaviour of the longer dry spells, and estimated increment in the number of heat waves during the months May to September (expressed in number of heat waves per 30 years)</td><td>18</td></tr><tr><td>Figure 8 Results for the climate change assessment of the Fire Weather Index (FWI) aggregated component, computed daily from 1980 to 2100 for the considered models. The entire time series has been estimated and the 90% quantile of each time period has been computed</td><td>22</td></tr><tr><td>Figure 9 Total cumulate burnt area (by large fires with more than 40 ha) as mapped by the European Forest Fires Information System (EFFIS) from 2000 to 2015</td><td>23</td></tr><tr><td>Figure 10 Results for the climate change assessment of the Drought Code (DC) component of the Fire Weather Index system, computed daily from 1980 to 2100 for the considered models. The entire time series has been estimated and the 95% quantile of each time period has been computed</td><td>24</td></tr><tr><td>Figure 11 Results for the climate change assessment of the Drought Code (DC) component of the Fire Weather Index system, computed daily from 1980 to 2100 for the considered models. The entire time series has been estimated and the 95% quantile of two time periods has been compared with the corresponding average values</td><td>25</td></tr><tr><td>Figure 12 Robustness of the estimated climate-driven change in the overall fire danger extremes. Ensemble analysis for the anomaly of the Fire Weather Index (FWI) aggregated component, computed daily from 1980 to 2100 for each climate-projection realisation. The difference between future and control period (anomaly) for extreme events of fire danger is assessed. The agreement among models on the change is shown</td><td>26</td></tr><tr><td>Figure 13 Robustness of the estimated climate-driven change in the fire danger extremes: focus on the effects of drier weather. Ensemble analysis for the anomaly of the Drought Code (DC) component of the FWI system, computed daily from 1980 to 2100. The difference between future and control period (anomaly) for extreme dry-weather events in fire danger is assessed. The agreement among models on the change is shown</td><td>27</td></tr><tr><td>Figure 14 Species richness may not be directly associated with the overall fire resistance and resilience of vegetation. Qualitative simplified representation, illustrating only two functional traits referring to corresponding fire resistance/resilience traits. Comparison between a vegetation community with higher functional richness and one with higher functional diversity</td><td>30</td></tr></table>
Figure 15 Causes of fire as reported by 19 European countries .................................... 34

Figure 16 Map of estimated wildfire damage value in Europe under three different scenarios of fire severity (low, medium and high fire severity) .................................................................... 39
<figure><img src="image_24.png" /></figure>

List of tables

<table><tr><td>Table 1 Main characteristics nominally associated with the fuel moisture codes of the Fire Weather Index (FWI) system</td><td>12</td></tr><tr><td>Table 2 Institutions and acronyms associated to the global circulation models and the regional climate models which define the EURO-CORDEX climate-projection realisations (model runs) considered in this study</td><td>20</td></tr><tr><td>Table 3 Short codes associated to each EURO-CORDEX climate-projection realisation (model run) considered in this study, and corresponding institutions, regional climate models (RCM) and driving Global Circulation Models (GCM)</td><td>20</td></tr><tr><td>Table 4 Time intervals associated to each period considered in the cluster of sectoral analyses within the PESETA III project.</td><td>20</td></tr><tr><td>Table 5 Empirical equations used in the second instance of the PESETA project series to estimate the expected central (average) values of burnt area by country</td><td>38</td></tr></table>
## GETTING IN TOUCH WITH THE EU

### In person

All over the European Union there are hundreds of Europe Direct information centres. You can find the address of the centre nearest you at: http://europa.eu/contact

### On the phone or by email

Europe Direct is a service that answers your questions about the European Union. You can contact this service:

- by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls),

- at the following standard number: +32 22999696, or

- by electronic mail via: http://europa.eu/contact

## FINDING INFORMATION ABOUT THE EU

### Online

Information about the European Union in all the official languages of the EU is available on the Europa website at: http://europa.eu

### EU publications

You can download or order free and priced EU publications from EU Bookshop at: http://bookshop.europa.eu. Multiple copies of free publications may be obtained by contacting Europe Direct or your local information centre (see http://europa.eu/contact).
## JRC Mission

As the science and knowledge service of the European Commission, the Joint Research Centre's mission is to support EU policies with independent evidence throughout the whole policy cycle.

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EU Science Hub

ec.europa.eu/jrc

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@EU_ScienceHub

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EU Science Hub - Joint Research Centre

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Joint Research Centre

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EU Science Hub

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Publications Office

doi:10.2760/13180

ISBN 978-92-79-77046-3