Canadian Forest Research Articles

Rapid response of stream dissolved phosphorus concentrations to wildfire smoke

Wildfires can produce large plumes of smoke that are transported across vast distances, altering nutrient cycling of undisturbed watersheds exposed downwind. To date, wildfire smoke influence on stream biogeochemical signatures remains an important knowledge gap. Here we evaluate the impacts of wildfire smoke on phosphorus (P) biogeochemical cycling in a temperate watershed in the Finger Lakes Region of Central New York located downwind from record setting Canadian forest fires during the summer of 2023. Daily sampling of stream and rainwaters was conducted over the 2 month smoke period, generating a robust geochemical dataset. Stream dissolved P showed high sensitivity to smoke events, attaining concentrations 2–3 × greater than the pre-smoke period. Subsequent rain events after smoke deposition were identified as a potentially important factor in magnitude and timing of dissolved P responses. These findings demonstrate the capacity for wildfire smoke to trigger rapid, observable changes to stream P chemistry.

Peat profile database from peatlands in Canada.

Peatlands cover approximately 12% of the Canadian landscape and play an important role in the carbon cycle through their centennial- to millennial-scale storage of carbon under waterlogged and anoxic conditions. In recognizing the potential of these ecosystems as natural climate solutions and therefore the need to include them in national greenhouse gas inventories, the Canadian Model for Peatlands module (CaMP v. 2.0) was developed by the Canadian Forest Service. Model parameterization included compiling peat profiles across Canada to calibrate peat decomposition rates from different peatland types, to define typical bulk density profiles, and to describe the hydrological (i.e., water table) response of peatlands to climatic changes. A total of 1217 sites were included in the dataset from published and unpublished sources. The CORESITES table contains site location and summary data for each profile, as well as an estimate of total carbon mass per unit area (in megagrams of C per hectare). Total carbon mass per unit area at each location was calculated using bulk density and carbon content through each profile. The PROFILES table contains data for depth (in centimeters), bulk density (in grams per cubic meter), ash and carbon content (in percentage), and material descriptions for contiguous samples through each peat profile. Data gaps for bulk density and C content were filled using interpolation, regression trees, and assigned values based on material description and/or soil classification to allow for the estimation of total carbon mass per unit area. A subset of the sites (N = 374) also have pH and pore water trace-elemental geochemistry data and are found in the WATER table. The REFERENCES table contains the full citation of each source of the data and is linked to each core location through the SOURCEDATA table. The LOOKUP table defines codes in the database that required more space that what was sufficient in the metadata tables. The data can be accessed on Open Government Canada and will be useful for future work on carbon stock mapping and ecosystem modeling. All metadata and data are provided © Her Majesty the Queen in Right of Canada, 2023 and information contained in this publication may be reproduced for personal or public noncommercial purposes with attribution, whereas commercial reproduction and distribution are prohibited except with written permission from NRCan; complete details are noted in the Supporting Information file Metadata S1 (see Class III.B.3: Copyright restrictions).

Development of national post-fire restoration system to assess net GHG impacts and salvage biomass availability

In light of the recent unprecedented wildfires in Canada and the potential for increasing burned areas in the future, there is a need to explore post-fire salvage harvest and restoration and the implications for greenhouse gas (GHG) emissions. Salvage logging and replanting initiatives offer a potential solution by regrowing forests more quickly while meeting societal demands for wood and bioenergy.This study presents a comprehensive modeling framework to estimate post-fire salvage biomass and net GHG emissions relative to a ‘do-nothing’ baseline for all of Canada's harvest-eligible forests. Forest ecosystem carbon emissions and removals were modeled at 1-ha spatial resolution for Canadian forests using the Generic Carbon Budget Model (GCBM) from 1990 to 2070 using several forest inventory data sources with future harvest and wildfires.Building upon previous research, our work integrated the Canadian Forest Fire Danger Rating System fire intensity to estimate fire severity of future wildfires. For 2024 to 2070, we assessed the changes in ecosystem carbon, emissions from harvested wood products, and substitution benefits from avoided emissions-intensive materials, relative to a forward-looking baseline. Our prototype system provides a comprehensive framework, configuration files, links to datasets to quantify the net GHG of post-fire restoration, and sample results for validation.•Developed spatially explicit forest carbon modeling system for all of Canada's forests.•Assessed the net GHG reduction from post-fire restoration.•Used system approach to consider forests, wood products and substitution benefits.

Carbon emissions from the 2023 Canadian wildfires

The 2023 Canadian forest fires have been extreme in scale and intensity with more than seven times the average annual area burned compared to the previous four decades1. Here, we quantify the carbon emissions from these fires from May to September 2023 on the basis of inverse modelling of satellite carbon monoxide observations. We find that the magnitude of the carbon emissions is 647 TgC (570–727 TgC), comparable to the annual fossil fuel emissions of large nations, with only India, China and the USA releasing more carbon per year2. We find that widespread hot–dry weather was a principal driver of fire spread, with 2023 being the warmest and driest year since at least 19803. Although temperatures were extreme relative to the historical record, climate projections indicate that these temperatures are likely to be typical during the 2050s, even under a moderate climate mitigation scenario (shared socioeconomic pathway, SSP 2–4.5)4. Such conditions are likely to drive increased fire activity and suppress carbon uptake by Canadian forests, adding to concerns about the long-term durability of these forests as a carbon sink5–8.

An Evaluation of a Winter Mortality Model for the Mountain Pine Beetle

The winter mortality of mountain pine beetle (Dendroctonus ponderosae Hopkins) larvae caused by temperatures below survivable thresholds and sudden decreases in temperature is among the most influential factors limiting population growth. Due to the importance of winter mortality in the demise of mountain pine beetle infestations, a widely used winter mortality model was developed at the Canadian Forest Service and the United States Forest Service. It predicts lethal temperatures and survival probabilities given temperature time series over the winter season. We present a rare and possibly the first peer-reviewed validation of this winter mortality model, wherein we independently tested the model at a new region in Canada by comparing model predictions to the observed lower lethal temperature thresholds and cold-associated mortality. Model predictions were biologically reasonable but slightly biased. Bias was exacerbated by the inaccurate translation of air temperature data from weather stations to temperatures under the bark where larvae develop. The spatial prediction of relative mortality observed across the study area in Banff National Park was poor—likely because the mountainous terrain presents a difficult prediction challenge when under-bark temperatures are not directly observed. Our results will help inform users of model constraints and how to optimize the accuracy of model predictions.

Extending Canadian forest disturbance history maps prior to 1985

AbstractAn accurate depiction of wildfire, harvesting, and insect outbreak disturbances is essential for sustainable ecosystem management of forests in Canada. Even though the advent of temporally consistent 30‐m resolution Landsat data has enabled the detailed mapping of forest disturbances in Canada from 1985 onward, the disturbance record prior to 1985 remains sparse. This study aimed to extend the existing pre‐1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multilayer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Our model detected approximately 4.8, 7.3, and 3.8 million ha of burnt, harvested, and insect‐ravaged forest areas, respectively, that were absent from national and provincial disturbance databases and forest inventories. Results were validated using both internal and external validation datasets. Our disturbance detection methodology was highly effective, with an internal validation kappa score of 0.91 and an external score of 0.81. The fire and harvest age disturbance MLPs, whose predictions can also be used as a proxy of forest stand age, performed adequately on the internal (fire R2 = 0.675; root mean squared error [RMSE] = 4.42; harvest R2 = 0.723; RMSE = 3.17) and external validation datasets (fire R2 = 0.242; RMSE = 4.69; harvest R2 = 0.257; RMSE = 5.46), outperforming existing forest age disturbance products. Finally, we relied on several open data products, such as provincial forest inventories, to correct our disturbance type and year prediction whenever these more reliable, but incomplete, data sources were available. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. We also illustrate how extending the existing forest disturbance record by 20 years may provide a more in‐depth understanding of landscape‐disturbance dynamics with a case study of the 2023 Canadian wildfire season.

Changes in aerosol properties at the El Arenosillo site in Southern Europe as a result of the 2023 Canadian forest fires

From the beginning of May 2023 to the end of August 2023, the Northern Hemisphere experienced significant wildfire activity with the most widespread fires occurring in Canada. Forest fires in Canada destroyed more than 15.6 million hectares of forests. These wildfires worsened air quality across the region and other parts of the world. The smoke reached southern Europe by the end of June 2023. To better understand the consequences of such forest fires far from the site of origin, aerosol optical, microphysical and radiative properties were analyzed during this event for southern Europe using data from the Visible Infrared Imaging Radiometer Suite (VIIRS), TROPOspheric Monitoring Instrument (TROPOMI), and Aerosol Robotic Network (AERONET). TROPOMI aerosol index (AI) and the carbon monoxide (CO) product confirm that the smoke originated directly from these forest fires. AERONET data from the El Arenosillo site in southern Spain showed maximum aerosol optical depth (AOD) values on June 27 reached 2.36. Data on Angstrom Exponent (AE), aerosol volume size distribution (VSD), single scattering albedo (SSA), fine mode fraction (FMF), volume particle concentration, effective radius (REff), absorption AOD (AAOD), extinction AE (EAE) and absorption AE (AAE) showed that fine-mode particles with carbonaceous aerosols contribution predominated in the atmosphere above the El Arenosillo site. Direct aerosol radiative forcing (DARF) at the top (DARFTOA) and bottom of atmosphere (DARFBOA) were −103.1 and −198.93 Wm−2, respectively. The atmospheric aerosol radiative forcing (DARFATM) was found to be 95.83 Wm−2 and with a heating rate 2.69 K day−1, which indicates the resulting warming of the atmosphere.

Advancing Forest-Fire Management: Exploring Sensor Networks, Data Mining Techniques, and SVM Algorithm for Prediction

Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.

Using the Weather Index Classification System for Canadian Forest Fires over the Al-Jabal Al-Akhdar in Libya

Using the Weather Index Classification System for Canadian Forest Fires over the Al-Jabal Al-Akhdar in Libya

PostBP: A Python library to analyze outputs from wildfire growth models

Wildfire is an important natural disturbance agent in Canadian forests, but it has also caused significant economic damage nationwide. Spatial fire growth models have emerged as important tools for representing wildfire dynamics across diverse landscapes, enabling the mapping of key wildfire hazard metrics such as location-specific burn probabilities or likelihoods of fire ignition. While these summary metrics have gained popularity, they often fall short in capturing the directional spread of wildfires and their potential spread distances. The metrics depicting the directional spread of wildfire can be derived from raw outputs generated with fire growth models, such as the perimeters and ignition locations of individual fires, but extracting this information requires complex data processing. To address this data gap, we present PostBP, an open-source Python package designed for post-processing the raw outputs of fire growth models — the ignition locations and perimeters of individual fires simulated over multiple stochastic iterations — into a matrix of fire spread likelihoods between all pairs of forest patches in a landscape. The PostBP also generates several other summary outputs, such as the source-sink ratio and the fire spread rose diagram. We provide an overview of PostBP's capabilities and demonstrate its practical application to a forested landscape.•Wildfire growth models generate large amounts of outputs, which are hard to summarize for practical decision-making.•The PostBP package calculates the summary metrics characterizing the directional spread of wildfires.•The fire risk summaries generated with PostBP can support the assessments of wildfire risk and mitigation measures.

Integration of Airborne Laser Scanning data into forest ecosystem management in Canada: Current status and future directions

Airborne Laser Scanning (ALS) has been the subject of decades of applied research and development in forest management. ALS data are spatially explicit, capable of accurately characterizing vegetation structure and underlying terrain, and can be used to produce value-added products for terrestrial carbon assessments, hydrology, and biodiversity among others. Scientific support for ALS is robust, however its adoption within environmental decision-making frameworks remains inconsistent. Cost continues to be a principal barrier limiting adoption, especially in remote, forested regions, however added challenges such as the need for technical expertise, unfamiliarity of data capabilities and limitations, data management requirements, and processing logistics also contribute. This review examines the current status of the integration of ALS data into forest ecosystem management in a Canadian context. We advocate for continued inter-agency acquisitions leading to integration of ALS into existing natural resource management decision pathways. We gauge the level of uptake thus far, discuss the barriers to operational implementation at provincial scales, and highlight how we believe ALS can support multiple objectives of forest and environmental management in Canada. We speak to potential benefits for supporting inter-agency terrain generation, ecosystem mapping, biodiversity assessments, silvicultural planning, carbon and forest health evaluations, and riparian characterizations. We conclude by providing key considerations for developing capacity using ALS and discuss the technologies future in the context of Canadian forest and environmental management objectives.

Tracking Carbon Dioxide with Lagrangian Transport Simulations: Case Study of Canadian Forest Fires in May 2021

The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment in other parts of the world. Numerical simulations of greenhouse gas transport processes are often affected by uncertainties in the location and timing of the emission sources and local meteorological conditions, and it is difficult to obtain accurate and credible predictions by combining remote sensing satellite data with given meteorological forecasts or reanalyses. To study the regional transport processes and impacts of greenhouse gases produced by sudden large-scale forest fires, this study applies the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) to conduct forward simulations of the CO2 transport process of greenhouse gases emitted from forest fires in the central region of Saskatchewan, Canada, during the period of 17 May to 25 May 2021. The simulation results are validated with the Orbiting Carbon Observatory-2 Goddard Earth Observing System (OCO-2 GEOS) Level 3 daily gridded CO2 product over the study area. In order to leverage the high computational costs of the numerical simulations of the model, we implement the forward simulations on the Tianhe-2 supercomputer platform and the JUWELS HPC system, which greatly improves the computational efficiency through parallel computation and makes near-real-time predictions of atmospheric transport processes feasible.

Synoptic-scale drivers of fire weather in Greece

The identification of the large-scale atmospheric circulation patterns which are associated with extreme fire weather is of great importance for developing early warning systems, management strategies, and for increasing awareness and preparedness of all the involved entities, including both the public and practitioners. Such a forecasting approach is currently missing in Greece and many other countries. Furthermore, considering climate projections over the Mediterranean, which indicate an environment more conducive to wildfire activity, the need for timely forecasting of extreme fire weather becomes increasingly urgent. Here, we present an alternative fire weather forecasting framework using ERA5 reanalysis data of atmospheric variables and fire weather indices of the Canadian Forest Fire Weather Index System (CFFWIS) during the period June–October from 1979 to 2019. Within this framework, we define the critical fire weather patterns (CFWPs) of Greece associated with different levels of fire weather severity by applying Self-Organizing-Maps (SOMs) on mid-tropospheric geopotential height. We quantify the fire weather conditions associated with each CFWP. Using a set of CFFWIS indices and key fire weather variables, our SOM-based analysis reveals five distinct CFWPs linked to different levels and characteristics of fire weather severity. The lowest fire weather severity is associated with lower than average geopotential heights, and anomalous cold and moist weather. The highest fire weather severity is associated with higher than average geopotential heights, and anomalous hot, dry, and windy conditions, suggesting the potential for wind-driven wildfires. Our analysis yields elevated fire weather severity linked to a CFWP, when hot and dry conditions are accompanied by atmospheric instability, suggesting the potential for plume-driven wildfires and the potential for pyroconvection. The main advantage of this forecasting framework is that it could be used for providing valuable information regarding the upcoming fire weather conditions even up to 7–12 days in advance depending on the atmospheric predictability.

Post-harvest regeneration is driven by ecological factors rather than wood procurement intensity in eastern Canadian forests

Abstract Biomass from surplus forest growth that is not harvested for wood supply of conventional industries can be an important source of feedstock for bioenergy. Its procurement can be integrated with little effort into current harvest operations. However, the increasing harvesting intensity to meet greater demand for biomass procurement can impact forest ecosystem functions because of its direct and indirect effects on woody debris and the regeneration of next-rotation stands. In this context, we aimed to determine the relationships between wood procurement intensity, woody debris inputs, and regeneration success over 2 years after harvesting. We tested four treatments of increasing wood procurement intensity using a randomized block design within six experimental sites along a gradient of varying forest characteristics of boreal and temperate forests. We assessed stand characteristics in terms of standing trees, woody debris, and regeneration pre- and post-harvest. We used mixed effects models to evaluate (i) the effects of wood procurement intensity and pre-harvest stand characteristics on the volume and cover of woody debris and (ii) the specific influence of woody debris on the presence of suitable planting microsites post-harvest. Furthermore, we used principal component regressions to explore the relationships between harvesting intensity and the presence of natural regeneration and competing vegetation as a function of pre- and post-harvest stand characteristics (iii). Our results showed that increasing wood procurement intensity reduced the volume of post-harvest woody debris while having a limited effect on regeneration. Increasing harvesting intensity had a negligible effect on suitable planting microsites in hardwood-dominated stands but it increased their presence in conifer-dominated stands. Natural regeneration and competing vegetation were mainly related to stand characteristics, and only broadleaf regeneration was sensitive to harvest intensity. We conclude that the relationships between wood procurement intensity and regeneration are complex and rely mainly on stand characteristics rather than wood procurement needs.

Mapping the distance between fire hazard and disaster for communities in Canadian forests.

Communities interspersed throughout the Canadian wildland are threatened by fires that have become bigger and more frequent in some parts of the country in recent decades. Identifying the fireshed (source area) and pathways from which wildland fire may ignite and spread from the landscape to a community is crucial for risk-reduction strategy and planning. We used outputs from a fire simulation model, including fire polygons and rate of spread, to map firesheds, fire pathways and corridors and spread distances for 1980 communities in the forested areas of Canada. We found fireshed sizes are larger in the north, where the mean distances between ecumene and fireshed perimeters were greater than 10 km. The Rayleigh Z test indicated that simulated fires around a large proportion of communities show significant directional trends, and these trends are stronger in the Boreal Plains and Shields than in the Rocky Mountain area. The average distance from which fire, when spreading at the maximum simulated rate, could reach the community perimeter was approximately 5, 12 and 18 km in 1, 2 and 3 days, respectively. The average daily spread distances increased latitudinally, from south to north. Spread distances were the shortest in the Pacific Maritime, Atlantic Maritime and Boreal Plains Ecozones, implying lower rates of spread compared to the rest of the country. The fire corridors generated from random ignitions and from ignitions predicted from local fire history differ, indicating that factors other than fuel (e.g. fire weather, ignition pattern) play a significant role in determining the direction that fires burn into a community.

Global patterns of tree wood density.

Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%-63% of spatial variations, followed by vegetation characteristics (25%-31%) and edaphic properties (11%-16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.

Study on Forest Fire Risk Analysis Based on Drought Scenarios

The number of forest fires is increasing worldwide owing to climate change. Forest fires linked to drought are becoming more frequent and severe than ever before. This causes massive damage. However, instead of quantification, studies have been conducted on the relationship between drought and forest fires. Therefore, this study quantitatively analyzed the increase in forest fire risk based on a drought scenario. We defined a 30-year frequency drought scenario for the Gyeongsangbuk-do Province, where the most forest fire damage occurred in 2022, and analyzed the risk of forest fires by calculating the Fire Weather Index (FWI) used in the Canadian Forest Fire Danger Rating System (CFFDRS). The precipitation in the drought scenario was 581 mm/year that was equivalent to 52% of the average annual precipitation. We noted that the drought Fire Weather Index (DFWI) was approximately 1.6 times greater than the average Fire Weather Index (AFWI). We quantitatively confirmed the extent to which the risk of forest fire increased under the influence of drought. Therefore, measures should be established not only for drought but also for forest fires when a drought occurs.

Climate sensitive growth and yield models in Canadian forestry: Challenges and opportunities

Growth and yield models in forest management planning are used to project future forest conditions and estimate quantities such as wood volume and biomass. These models are crucial for assessing forest sustainability, however, some models currently used in Canada do not adequately account for climate and other environmental variables, which limits their effectiveness under a changing climate. Climate-sensitive growth and yield models (CSGYMs) are therefore urgently needed to support forest management decisions. The Canadian Forest Service (CFS) has developed a strategic plan to advance climate-sensitive growth and yield modeling in Canada through collaboration with provincial and territorial agencies, as well as other partners. The primary objective of this plan is to provide a national-level modelling approach to predicting and managing forest growth, mortality, and other ecosystem services. The climate sensitive growth and yield modelling initiative emphasizes collaboration, open data, and open-source code principles to ensure widespread accessibility and uptake of models, thus contributing to the sustainable management of forest resources. This technical review reports on the status of growth and yield models currently applied in each province and territory, assesses the level of climate sensitivity associated with each of these models, synthesizes the relevant modeling approaches and input data required to implement climate sensitivity into these models, and suggests possible pathways for achieving CSGYM at a national scale. Widespread collaboration will be the key to advancing the development of CSGYMs.

Human Impact Land Use Layer from the Canadian Forest Service National Forest Inventory

The Human Impact Land Use Layer (HILL) dataset was developed with visual interpretation of high-resolution satellite imagery. 26,388 2 × 2 km photo plots from the Canadian National Forest Inventory program were examined for any visible evidence of human impact within and outside of the photo plot boundary. The interpreter provided a detailed description of all human impacts and a measure of the total area of all the impacts in the plot. Human impact classes were developed from the interpreter’s plot descriptions. 45.7% of the photo plots have evidence of human impact within, or close to, the plot boundary. The HILL dataset could be used as a validation layer for human impact map products produced using Geographic Information System processes.

Climate change impacts on regional fire weather in heterogeneous landscapes of central Europe

Abstract. Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2022 and 2023 demonstrated the destructive power of wildfires, especially in North America and southern Europe. Global warming leads to changes in fire danger. Specifically, fire seasons are assumed to become more extreme and will extend to more temperate regions in northern latitudes in the future. However, the extent to which the seasonality and severity of fire danger in regions of central Europe will change in the future remains to be investigated. Multiple studies claim that natural variability and model uncertainty hide the trend of increasing fire danger in multi-model climate simulations for future potentially fire-prone areas. Such a trend might be isolated with single-model initial-condition large ensembles (SMILEs), which help scientists to distinguish the forced response from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. To date, only a few dynamically downscaled regional SMILEs exist, although they enhance the spatial representation of climatic patterns on a regional or local scale. In this study, we use a regional SMILE of the Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE) over a region in central Europe under the RCP8.5 (Representative Concentration Pathway) scenario from 1980 to 2099 to analyze changes in fire danger in an area that is currently not fire prone. We use the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the South German Escarpment, and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a dataset suitable for disentangling climate trends from natural variability in a multi-variate fire danger metric. Our results show the strongest increases in the median (50th) and extreme (90th) quantiles of the FWI in the northern parts (South German Escarpment and Eastern Mountain Ranges) of the study area in the summer months of July and August. There, high fire danger becomes the median condition by the end of the century, and levels of high fire danger occur earlier in the fire season. The southern parts (Alps and Alpine Foreland) are less strongly affected by changes in fire danger than the northern parts. However, these regions reach their time of emergence (TOE) in the early 2040s because of very low current fire danger. In the northern parts, the climate change trend exceeds natural variability only in the late 2040s. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by the end of the century. Our results highlight the potential for severe future fire events in central Europe, which is currently not very fire prone, and demonstrate the need for fire management even in regions with a temperate climate.