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Weather and climate play an important role in shaping global fire regimes and geographical distributions of burnable areas. At the global scale, fire danger is likely to increase in the near future due to warmer temperatures and changes in precipitation patterns, as projected by the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). There is a need to develop the most reliable projections of future climate-driven fire danger to enable decision makers and forest managers to respond to future fire events.Climate change projections generated by general circulation models, especially those that contribute to the 6th Coupled Model Intercomparison Project (CMIP6), are the most important basis in understanding future changes in fire-conducive weather and climate associated with a warming world. However, errors and biases inherent to such models are rarely taken into account when generating climate change projections. For fire weather in particular, projections have typically been expressed by a single model or through a multi-model mean. This approach can be misleading, as it explains little about the consensus among different models and their uncertainties. Here, following a comprehensive evaluation of the performance of 16 different CMIP6 climate model ensembles, we present new scenarios for detecting changes in fire-prone conditions based on a statistical weighting approach that accounts for both model skill and independence. We demonstrate the value of a weighted approach in accounting for and reducing model uncertainties, and more generally in the development of fire weather scenarios that ultimately as useful as possible. In conclusion, we make recommendations for how the new set of scenarios can benefit end users in decision-making and forest management.

In recent years, the occurrence of a series devastating wildfire events around the world has raised considerable public concern about how climate change is altering meteorological conditions conducive to such events. The relative scarcity of wildfire attribution studies, coupled with the limited observational record, has added to the difficulty of developing reliable collective conclusions for future forest management strategies. Recent work has discussed the uncertainties and sensitivities associated with the choice of meteorological indicator to represent fire weather and the validation of climate model ensemble in the context of extreme event attribution, but the value of linking the attribution of recent record-breaking wildfire events with future risk assessment has not yet been fully explored.Here, using an established probabilistic framework based on extreme value theory, we present the findings of attribution analysis applied to a series of recent high-impact wildfire. In each case, fire weather extremes, represented by annual maxima of the Canadian Fire Weather Index (FWI), are fitted to an extreme value distribution and scaled to the global mean surface temperature. Probability ratios are used to quantify the influence of rising global temperatures on the changing frequency of FWI extremes for past, present and future climates. We demonstrate the value of the application of a common methodological framework in combining results from different case studies as part of a collective attribution approach for multiple extreme across several world’s fire-prone regions. Further analysis of future risks will provide robust recommendations to reduce and address the hazards posed by wildfires and to improve post-disaster resilience.

African countries are highly vulnerable to floods, with several studies reporting an increase in mortality rate and exposure in recent decades. Therefore, to improve flood forecasting and associated resilience to such natural hazards, a better understanding of the dominant flood-generating mechanisms and their evolution across Africa is of paramount importance. According to a recent study, excess rains on saturated soils in western Africa, and long rains for catchments in northern and southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. While the dominance of flood-generating mechanisms was not reported to change significantly in recent decades, the magnitude of those events may have changed in response to changing rainfall patterns (wetter or drier average conditions) across Africa. Here, we first estimate how much the magnitude of events with excess rainfall on saturated soils and long rains have changed over the last 65 years, before examining the statistical relationship between these changes, globally warming temperatures, and “natural” modes of decadal climate variability (e.g., Atlantic Multidecadal Variability [AMV], Pacific Decadal Variability [PDV]). To do so, we use a non-linear extreme value modelling approach with multiple covariates, applied to multiple observational datasets (e.g., ERA5, REGEN) and large ensembles from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). This study, therefore, contributes to further the understanding of recent flood hazards in Africa, and identifies regions that will likely become more vulnerable to climate change and its decadal variability over the decades to come.

The agriculture sector is sensitive to changes in weather and climate, notably extreme events. Extreme variations in weather conditions throughout a growing season led to changes in phenological features of vegetation and crops and cause variations in harvest, and, generally, the impact could be large in terms of production amounts and then food security in the region. The last version of the Intergovernmental Panel on Climate Change report (IPCC) highlighted the southern Mediterranean region as a hot spot and one of the most vulnerable regions in the world. While numerous studies have addressed changes in climate extremes throughout the world, very little research has been conducted in the southern Mediterranean region concerning the seasonal concurrence of climate extremes, and their evolutions in recent decades. Moreover, understanding the impacts of these changes on vegetation phenology is crucial in the region, but this is yet to be studied.This study evaluates the impact of climate extremes on vegetation phenology in the southern Mediterranean region over the last 40 years. Firstly, the trends of 15 phenological vegetation indicators (e.g., length of the growing season, maximum Normalized Difference Vegetation Index [NDVI] value/time, onset/offset times, green upslope, brown downslope, etc.) are examined using National Oceanic and Atmospheric Administration [NOAA] satellite images and the modified Mann-Kendall trend test. Secondly, we examine how recent trends in vegetation phenology compare with those observed in various heat-related indices (heat wave characteristics), water-related indices (Standard Precipitation Index [SPI], Standard Precipitation Evapotranspiration Index [SPEI], Extreme Precipitation, Wet/Dry spells), and compound indices (Dry-Cold, Dry-Hot, Wet-Cold, Wet-Hot events) calculated using the ERA5-land dataset. Finally, we examine the relative importance of each climate indicator in explaining multi-year changes in vegetation phenology. As such, this study not only identifies the areas with higher risk and vulnerability for vegetation and crops in the last years but also identifies potential predictors for seamless seasonal-to-decadal forecasts of agrometeorological risks across the region.

Sub-Saharan Africa is affected by a high level of temporal and spatial climate variability, with large impacts on water resources, human lives, and economies, notably through hydrological extremes such as floods. Nevertheless, the key climatic factors driving interannual variability in flood frequency remain poorly documented and understood. To address this research gap, we first compile information on large-scale climate drivers that may potential affect sub-Saharan African hydroclimate (e.g., El Niño–Southern Oscillation, Atlantic Multidecadal Variability). Then, using a new 65-year long daily streamflow dataset of over 600 stations in sub-Saharan Africa, a bootstrapped stepwise regression and relative importance analysis is applied to quantify the relative contribution of different ocean basins to interannual variability in flood frequency between 1950 and 2014. Results show that interannual variations in the frequency of flood events are significantly linked to different modes of climate variability in the Pacific, Indian, and Atlantic Oceans. These modes of climate variability together explain around 60% of observed interannual variation in seasonal flood frequency. The relative influence of each ocean basin, however, differs from one region to another. The Indian and Pacific Oceans, for instance, have significant influences on interannual variations in the frequency of floods between December and May across much of southern and eastern Africa. In western Africa, the Mediterranean and Atlantic Oceans appear to have a dominant influence between September and November. In central Africa, the relative influence of different oceans basins is seasonally variable. Using the best combination of Sea-Surface Temperature predictors, we then examine projected future trends using a large ensemble of climate models from the CMIP6 experiments.

Sub-Saharan Africa is affected by a high-level of temporal and spatial variability in climate, with large impacts on water resources, human lives and economies, notably through hydrological extremes, such as floods and droughts. Using a newly reconstructed 65-year daily streamflow dataset of over 600 stations distributed throughout sub-Saharan Africa, we first highlight that the frequency, intensity and duration of hydrological extremes are strongly impacted by decadal to multi-decadal variations. However, the key factors driving such decadal to multi-decadal variability remain poorly documented and understood. Thus, here, compiling information on local-scale drivers (precipitation, temperature, soil moisture) and large-scale drivers (sea-surface temperature modes of variability, such ENSO and AMO, in the different ocean basins), using relative importance analysis and multiple datasets (ERSSTv3, ERA5-land, REGEN), we investigate the respective contribution of large-scale versus regional-scale processes in driving decadal to multi-decadal variability in long-lived and short-lived flood and drought.

Sub-Saharan Africa is affected by a high-level of temporal and spatial climate variability, with large impacts on water resources, human lives and economies, notably through hydrological extremes, such as floods and droughts. Using a newly reconstructed 65-year daily streamflow dataset of over 600 stations distributed throughout sub-Saharan Africa, we first highlight that the frequency, intensity and duration of hydrological extremes are strongly impacted by decadal to multi-decadal variations. However, the key factors driving such decadal to multi-decadal variability remain poorly documented and understood. To address this research gap, we first compile information on local-scale (precipitation, temperature, soil moisture) and large-scale (e.g., El Niño–Southern Oscillation, Atlantic Multidecadal Variability) drivers. Then, by using relative importance analysis and multiple datasets, we investigate the contribution of large-scale versus regional-scale processes in driving decadal to multi-decadal variability in floods and droughts. Results show that the changes in flood and drought characteristics are significantly linked to modes of climate variability in the Pacific, Indian, and Atlantic Oceans. Although flood and drought characteristics are significantly correlated, the influences of large-scale climate variability on them are non-linear. Meanwhile, local-scale factors impacting floods and droughts are variable throughout the sub-continent. Our results highlight the role that changes in rainfall, soil moisture and temperature play across the major watersheds in sub-Saharan Africa.

In response to the occurrence of a number of large wildfire events across the world in recent years, the question of the extent to which climate change may be altering the meteorological conditions conducive to wildfires has become a hot topic of debate. Despite the development of attribution methodologies for extreme events in the last decade, attribution studies dedicated explicitly to wildfire, or otherwise extreme ‘fire weather’, are still relatively few. In turn, there is a lack of consensus on how to define fire risk in a meteorological context, posing a challenge for research in this subfield. Recent work has offered clarification on uncertainties associated with the choice of meteorological indicator to represent fire weather in the context of extreme event attribution but there are additional sensitivities that are still not fully understood.Here, using established statistical methodologies applied to six large (>10-member) CMIP6 model ensembles, we conduct probabilistic attribution of fire weather extremes across the world’s fire-prone regions. We assess trends in extremes in the Canadian Fire Weather Index (FWI) using extreme value distributions, fitted with both annual maxima and peaks over a pre-defined threshold, and scaled to global mean surface temperature. An initial evaluation of model performance shows that, while all models are able to reasonably reproduce observed global patterns in extreme distribution parameters, there are some notable differences at the regional scale. Subsequently, we use probability ratio maps to quantify the influence of rising global temperatures on the changing frequency of FWI extremes. Our results highlight the sensitivity of probabilistic fire weather attribution methodologies to the choice of climate model ensemble. In conclusion, we therefore make a set of recommendations for future attribution of extreme fire weather episodes: (i) the use (and comparison) of multiple model ensembles; (ii) robust evaluation of model capacity to represent fire weather extremes.

Climate change, as one of the most significant challenges that humans currently face, is defined as a shift in climate patterns in response to increasing greenhouse gas emissions in the atmosphere. Notably, climate change has been associated with global warming temperature, regional changes in rainfall patterns and extreme events. Such changes in climate are emerging at a time of rapid growth for many economies in the southern Mediterranean region, stressing the need to understand their impacts on sectors better. In addition, unlike other regions, very little research has been undertaken to understand how climate has changed (and will change) over the area and how such changes may affect the agricultural sector. Here, using trend analysis and the non-stationary generalized extreme value (GEV) model, we examine whether the probability of extreme agrometeorological risks has changed over the last 60 years in response to the globally warming temperature. We then quantify the magnitude of such changes over different phenological stages for wheat, maize and rice, highlighting the most vulnerable areas to extreme conditions in the southern Mediterranean region. Extreme agrometeorological risks are estimated using multiple state-of-the-art observational and reanalyzed daily datasets (REGEN, BERKELEY, CHIRPS, ERA5 and ERA5-land), and using multiple drought indices (standardized precipitation-evapotranspiration index [SPEI], standardized antecedent precipitation-evapotranspiration index [SAPEI], frequency and duration of dry spells) and heat stress indices (wet-bulb globe temperature [WBGT], effective temperature, discomfort index, heat index, frequency and duration of hot and cold spells). As such, this study, identifying areas in which crop growth and productivity are becoming particularly threatened by the increasing frequency and duration of extremes, provides vital evidence for climate change adaptation and mitigation plans in the southern Mediterranean region.

Africa is affected by a high-level of temporal and spatial variability in climate, with large impacts on water resources, human lives and economies. Due to data scarcity, the impact of multi-year climate variations on hydrological variability and extremes, i.e. flood and drought, as well as how catchment properties could modulate those impacts, are generally poorly understood across the African continent. In this study, we first use machine learning algorithms to develop a new complete reconstructed daily streamflow dataset using more than 1500 stream gauges between 1950 and 2018, and covering most of Africa. We then examine historical trends and variability in hydrological extremes over the entire African continent, focusing on different hydrological characteristics, such as the timing, frequency and duration of high- and low-flow events, based on the peaks-over-threshold method. Following an assessment of the relative sensitivities of hydrological extreme indices to interannual (2-8-years) and decadal (>10-years) variability in the different regions of Africa, we analyze the respective contribution of different rainfall, temperature and soil moisture indices (e.g. frequency, duration and intensity of wet/dry or warmer/colder days) at both timescales, using relative importance analysis. We finally discuss how catchment properties (e.g. area, topography, land use/ land cover, drainage path lengths) modulate the relationship between hydrological extremes and climate.

Weather and climate play an important role in shaping global fire regimes and geographical distributions of burnable areas. At the global scale, fire danger is likely to increase in the near future due to warmer temperatures and changes in precipitation patterns, as projected by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). There is a need to develop the most reliable projections of future climate-driven fire danger to enable decision makers and forest managers to take both targeted proactive actions and to respond to future fire events.Climate change projections generated by Earth System Models (ESMs) provide the most important basis for understanding past, present and future changes in the climate system and its impacts. ESMs are, however, subject to systematic errors and biases, which are not fully taken into account when developing risk scenarios for wild fire activity. Projections of climate-driven fire danger have often been limited to the use of single models or the mean of multi-model ensembles, and compared to a single set of observational data (e.g. one index derived from one reanalysis).Here, a comprehensive global evaluation of the representation of a series of fire weather indicators in the latest generation of ESMs is presented. Seven fire weather indices from the Canadian Forest Fire Weather Index System were generated using daily fields realisations simulated by 25 ESMs from the 6th Coupled Model Intercomparison Project (CMIP6). With reference to observational and reanalysis datasets, we quantify the capacity of each model to realistically simulate the variability, magnitude and spatial extent of fire danger. The highest-performing models are identified and, subsequently, the limitations of combining models based on independency and equal performance when generating fire danger projections are discussed. To conclude, recommendations are given for the development of user- and policy-driven model evaluation at spatial scales relevant for decision-making and forest management.

Wildfires constitute a major natural hazard and pose huge risk to many regions of the world. The series of large fires across both hemispheres in recent years have led to inevitable questions about how human-induced climate change may be altering the character of such events. Providing answers to these questions is a crucial step to increasing resilience to major wildfires.Long-term projections produced by state-of-the-art climate models, even when reliable, are not always a suitable means of communicating risk. Methodologies to attribute trends in meteorological phenomena associated with high-impact events to anthropogenic influence have the potential to better communicate risk and guide adaptation strategies. While the link between a warming world and heat-related extremes (e.g. heatwaves and droughts) is reasonably well-understood, there is a lack of consensus on the most appropriate and effective methodological approach for many variables, potentially impacted by warming climate, such as wildfire attribution. The link with climate change remains poorly understood and wildfires have been largely ignored by attribution studies to date.As a first step towards the development of a seamless, globally-applicable framework for assessing past, present and future risk in wildfire danger, we present a global attribution analysis of wildfire danger. With initial focus on observational records, we use both established and novel empirical-statistical methods to attribute historical trends in episodes of extreme weather and climate conducive to wildfire ignition and spread. Particular consideration is given to the sensitivity of attribution findings to the spatial scale upon which the analysis is conducted. We also draw attention to a series of important, often overlooked, conceptual and technical challenges in event attribution, including validation and bias-correction of climate models and discuss the value of linking attribution of recent wildfire events with future risk assessment.

It is now widely recognized that El Nino-Southern Oscillation (ENSO) occurs in more than one form, e.g. eastern and central Pacific ENSO. Given that these various ENSO flavours may contribute to climate variability and trends in different ways, this study presents a framework that treats ENSO as a continuum to examine its impact on precipitation, and to evaluate the performance of the last two generations of global climate models (GCMs): CMIP5 and CMIP6.Uncertainties in the location and intensity of observed El Nino and La Nina events are assessed in various observational and satellite-derived products (ERSSTv5, COBESSTv2, HadSST1 and OISSTv2). The probability distributions of El Nino and La Nina event locations, and intensities, slightly differ from one observational data set to another. For instance, La Nina events are more intense and more likely to occur in the central Pacific using COBESSTv2. All these products also depict consistent decadal variations in the location and intensity of ENSO events: i) central Pacific ENSO events were more likely in the 1940/50s and from the 1980s; ii) eastern Pacific ENSO events were more likely in the 1910/20s and 1960/70s; iii) La Nina events have become more intense during the 20th and early 21st centuries.These fluctuations in ENSO location and intensity are found to impact precipitation consistently across diverse global precipitation products (CRUv4.03, GPCCv8 and UDELv5.01). Over southern Africa, for instance, more intense eastern (central) Pacific El Nino events are found to favour drought conditions over northern (southern) regions during austral summer. By contrast, over the same regions, more intense La Nina events favours wet conditions, while the location of these events has little effect on precipitation. Over West Africa, ENSO locations favour a zonal (E-W) rainfall gradient in precipitation during boreal summer, while changes in ENSO intensity modulate the strength of the meridional (N-S) rainfall gradient.Using both historical and pi-Control runs, we demonstrate that most CMIP5 and CMIP6 models favour either eastern or central Pacific ENSO events, but very few models are able to capture the full observed ENSO continuum. Regarding ENSO impacts on worldwide precipitation, contrasted results appear in most models.

While there is a discernible global warming fingerprint in the increase observed daily temperature extremes, there is far greater uncertainty of the role played by anthropogenic climate change with regard to extreme precipitation. A logical progression of thought is that an increase in extreme precipitation results from the 7% increase in atmospheric moisture per 1°C global temperature increase predicted by the Clausius-Clapeyron (CC) relation. While this is supported by observations on the global scale, rates of extreme precipitation at smaller spatial and temporal scales are influenced to a far greater extent by atmospheric circulation and vertical stability in addition to local moisture availability. Many of these processes and other features of extreme precipitation events are not sufficiently represented in general circulation model (GCM) simulations. Meanwhile, limited observational networks mean that many short-term convective events are not accurately represented in the observational data. Errors and biases are common to all global and regional climate models, and many users of climate information require some form of statistical correction to improve the usefulness of model output. As so-called bias correction has become commonplace in climate impact research, its development has been hastened by a sustained debate regarding model correction in general leading to techniques that merge statistical correction and downscaling, represent random variability using stochasticity and are explicitly applicable to extremes. To date, attribution of extreme precipitation has not fully utilised the tools available from recent advances in bias correction, stochastic postprocessing and statistical downscaling. In the same way that GCMs are the most important tool in making climate change projections, understanding the degree to which the nature of a particular weather event has changed due to global warming requires long-term simulations of global climate from the pre-industrial era to the present day. The lack of a correction and/or downscaling step in almost all precipitation event attribution methodologies is therefore surprising. Here, we present a multi-scale attribution analysis of a sample of extreme precipitation events across Europe using a blend of observation- and model-based data. Attribution information generated using the raw output of global and regional climate model ensembles will be compared to that generated using the same set of models following a statistical postprocessing and downscaling step. Our conclusions will make recommendations for the value and wider application of downscaling methodologies in attribution science.

Fire is the primary driving factor of the ecosystem dynamics of many forests, directly affecting the global carbon balance and atmospheric concentrations of the trace gases including carbon dioxide. Recent anthropogenic influence has led to an increase in frequency and impact of wild fires. Hence, it is of vital importance to predict forest fire risk at monthly and seasonal time scales in order to mitigate its impacts, including fire driven dynamics of ecosystem and socio-economic services.Resilience of the ocean–atmosphere system provides potential for early detection of upcoming fire season intensity. Here, we report on the development of a probabilistic empirical prediction system for forest fire risk on monthly to seasonal timescales across the Northern Hemisphere, using local and large scale climate information as predictors for future fire weather. The fire risk is quantified by the monthly drought code (MDC), which is an established indicator for seasonal fire activity.The forecasts are disseminated through the KNMI climate explorer, using an interactive online Python application, in order to convey forecast information in a simple and digestible manner. A forecasting page allows for end-users to assess local seasonal fire weather risk, associated forecast skill, and the relation between historical MDC and observed fires. The forecasts are updated monthly throughout the fire season. A research page allows for local and global analysis of the sources of predictability, and characterization of the patterns of spatial and temporal variability of fire weather risk.

Fire remains the main natural disturbance factor in the European boreal zone (EBZ), which exhibits strong gradients in climate conditions, modern and historical patterns of forest use, and the modern human infrastructure density. Understanding climatic forcing on fire activity is important for projecting effects of climate change on multiple ecosystem services in this region. Here we analyzed available records of annually burned areas (ABA) in 16 administrative regions of EBZ (countries or sub-country units) and fire weather variability to test for their spatio-temporal patterns over 1901-2017. To define sub-regions of EBZ with similar fire activity we compiled 30-60 year long ABA chronologies and clustered them in Euclidian space to identify regions of EBZ with temporally synchronous fire activity. We then reconstructed 100-year long ABA chronologies for each cluster, capitalizing on its member with the highest correlation between observational fire record and climatological fire weather proxy (MDC, monthly drought code). The 100-year chronologies helped identified large fire years (LFY), i.e. years with the ABA being above 10% of its long-term distribution. The climatic forcing of these events was tested in superposed epoch analysis operated with gridded 500 hPa pressure fields. Finally, we tested trends in (a) synchrony of LFY's across clusters, (b) MDC values over the EBZ, and (c) spatial variability in July MDC over the EBZ geographic domain during 1901-2017.EBZ exhibits large variability in forest fire activity with the fire cycles varying from ~104 (Scandinavia) to 3*102 years (Russian Republic of Komi). Clustering of administrative units in respect to their ABA suggested the presence of sub-regions with synchronous dynamics of ABA, located along W-E and S-N gradients. LFYs in each of the cluster was associated with the development of the high pressure cell over the regions in question in July, indicating climatic forcing of LFYs. Contingency analysis indicated no long-term trend in the synchrony of LFYs observed simultaneously in several administrative units. We observed a trend towards higher values of MDC for the months of April and May in the western section of EBZ (April) and southern-eastern sections of the Baltic sea region and North sections of EBZ in Russia (May). Trends in MDC during the summer months were largely absent. We discuss teleconnections of fire activity in the EBZ with Atlantic SST.