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Compound dry and hot events can cause aggregated damage compared with isolated hazards. Although increasing attention has been paid to compound dry and hot events, the persistence of such hazards is rarely investigated. Moreover, little attention has been paid to the simultaneous evolution process of such hazards in space and time. Based on observations during 1961–2014, the spatiotemporal characteristics of compound long-duration dry and hot (LDDH) events in China during the summer season are investigated on both a grid basis and a 3D event basis. Grid-scale LDDH events mainly occur in eastern China, especially over northeastern areas. Most regions have experienced a pronounced increase in the likelihood of LDDH events, which is dominated by increasing temperatures. From a 3D perspective, 146 spatiotemporal LDDH (SLDDH) events are detected and grouped into 9 spatial patterns. Over time, there is a significant increase in the frequency and spatial extent of SLDDH events. Consistent with the grid-scale LDDH events, hotspots of SLDDH events mainly occur in northern China, such as the Northeast China, North China and Qinghai clusters, which are accompanied by a high occurrence frequency and large affected areas greater than 300 000 km2.

The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.

Long-duration, sub-seasonal dry spells in combination with high temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 (coupled model intercomparison project 5) GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a consecutive number of days with a daily precipitation of less than 1 mm. CMIP5 models tend to underestimate the persistence of dry spells in northern Europe, while a large variability exists between model estimates in central and southern Europe, where models have contrasting biases. Throughout Europe, we also find a large spread between models in their representation of temperature extremes during dry spells. In central and southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Our results indicate that this variability in model estimates is due to model differences and not internal variability. At latitudes between 50–60∘ N, the differences in the representation of persistent dry spells are strongly related to the representation of persistent anticyclonic systems, such as atmospheric blocking and subtropical ridges. Furthermore, models simulating a higher frequency of anticyclonic systems than ERA5 also simulate temperatures in dry spells that are between 1.4, and 2.8 K warmer than models with a lower frequency in these areas. Overall, there is a large spread between CMIP5 models in their representation of long-duration dry and hot events that is due to errors in the representation of large-scale anticyclonic systems in certain parts of Europe. This information is important to consider when interpreting the plausibility of future projections from climate models and highlights the potential value that improvements in the representation of anticyclonic systems may have for the simulation of impactful hazards.

Climate scientists have proposed two methods to link extreme weather events and anthropogenic climate forcing: the probabilistic and the storyline approach. Proponents of the first approach have raised the criticism that the storyline approach could be overstating the role of anthropogenic climate change. This issue has important implications because, in certain contexts, decision-makers might seek to avoid information that overstates the effects of anthropogenic climate change. In this paper, we explore two research questions. First, whether and to what extent the storyline approach overstates the effects of anthropogenic climate change. Second, whether the objections offered against the storyline approach constitute good reasons to prefer the probabilistic approach. Concerning the first question, we show that the storyline approach does not necessarily overstate the effects of climate change, and particularly not for the reasons offered by proponents of the probabilistic approach. Concerning the second question, we show, independently, that the probabilistic approach faces the same or very similar objections to those raised against the storyline approach due to the lack of robustness of climate models and the way events are commonly defined when applying the probabilistic approach. These results suggest that these objections might not constitute good reasons to prefer the probabilistic approach over the storyline approach.

Debris-flow activity is strongly controlled by hydro-meteorological trigger conditions, which are expected to change in a future climate. In this study we connect a regional hydro-meteorological susceptibility model for debris flows with climate projections until 2100 to assess changes of the frequency of critical trigger conditions for different trigger types (long-lasting rainfall, short-duration storm, snow-melt, rain-on-snow) in six regions in the Austrian Alps. We find limited annual changes of the number of days critical for debris-flow initiation when averaged over all regions, but distinct changes when separating between hydro-meteorological trigger types and study region. Changes become more evident at the monthly/seasonal scale, with a general trend of critical debris-flow trigger conditions earlier in the year. The outcomes of this study serve as a basis for the development of adaption strategies for future risk management.

Human society and natural systems are intrinsically adapted to the local climate mean and variability. Therefore, changes relative to the local expected variability are highly relevant for assessing impact and planning for adaptation as the climate changes. We analyse the emerging climate signal relative to the diagnosed internal variability (signal-to-noise ratio, S/N) of a set of recently published climate indices over Europe. We calculate the signal-to-noise ratio with respect to a recent baseline (1951–1983) which relates to recent societal experience. In this framework, we find that during the 2000–2016 period, many areas of Europe already experienced significant changes in climate extremes, even when compared to this recent period which is within living memory. In particular, the S/N of extreme temperatures is larger than 1 and 2 over 34% and 4% of Europe, respectively. We also find that about 15% of Europe is experiencing more intense winter precipitation events, while in summer, 7% of Europe is experiencing stronger drought-inducing conditions.

In the June of 2009 central Europe witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in the southeastern Austrian state of Styria, and property damages worth €10 Million. Elsewhere in Austria, flooding amounted to reparations worth €40 Million. Numerous synoptic-scale studies indicated the presence of a cut-off low over central Europe and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may manifest into a more intense event due to the higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation, more so in a topographically heterogeneous region such as the GAR (Greater Alpine Region).The flooding and landslides caused in the region raise an alarm and thus motivate this study whereby we investigate if the rainfall event did become stronger with time due to climate change compared to how it would have been in a counterfactual (climate change free) past. Here we have deployed the CCLM high-resolution regional model coupled with a statistical landslide model to simulate this event (rainfall and landslides) in a pseudo (surrogate) warming scenario. A marked decrease in rainfall intensity is observed in the simulations for a 1 K cooler climate (pre-industrial past) and the consequent landslide risk could reduce by up to 20%. In the future, depending on the changes in rainfall and soil moisture, the area affected during a 2009-type event could grow by 45% at 4 K global warming, although a slight reduction is also possible.In this novel event-based study, we discuss the results from the lens of attribution perspective - how conditional attribution is much more useful compared to the conventional risk-based approach of attributing extreme events. We develop physical storylines to address the increasing risks of landslides in the region.

With changing environmental conditions, the risk of landslides will also change. For the Styrian basin, Austria, we investigate how storylines of climate and land use/land cover change may affect future landslide susceptibility (2071-2100). Our analysis is based on two extreme rainfall events in Styria in 2009 and 2014, which triggered more than three thousand landslides causing a major threat to the local population and significant damage to settlements and infrastructure.Furthermore, while the number of studies analysing the impact of climate and land use change on landslide dynamics is rising, the assessment of their uncertainties is still often neglected. However, the quantification of uncertainties is not only essential for the development of business strategies and policy interventions, but also for increasing transparency and confidence in scientific analysis. Therefore, we additionally analyse the joint contribution of climate change uncertainty and landslide model uncertainty for the developed storylines of landslide susceptibility.We found for the worst-case storyline (4 K warming scenario) a substantial increase in highly susceptible areas due to much heavier rain. However, the estimated prediction uncertainties were generally high in all storylines. We discovered that the parametric landslide model uncertainty was of the same order as the climate scenario uncertainty, while uncertainties due to internal climate model variability were negligible. With an improved availability of event-based landslide inventories and high-resolution ground data, uncertainties in storylines of landslide susceptibility may be reduced.

Extended periods without precipitation, observed for example in central Europe including Germany during the seasons from 2018 to 2020, can lead to water deficit and yield and quality losses for grape and wine production. Irrigation infrastructure in these regions to possibly overcome negative effects is largely non-existent. Regional climate models project changes in precipitation amounts and patterns, indicating an increase in frequency of the occurrence of comparable situations in the future. In order to assess possible impacts of climate change on the water budget of grapevines, a water balance model was developed, which accounts for the large heterogeneity of vineyards with respect to their soil water storage capacity, evapotranspiration as a function of slope and aspect, and viticultural management practices. The model was fed with data from soil maps (soil type and plant-available water capacity), a digital elevation model, the European Union (EU) vineyard-register, observed weather data, and future weather data simulated by regional climate models and downscaled by a stochastic weather generator. This allowed conducting a risk assessment of the drought stress occurrence for the wine-producing regions Rheingau and Hessische Bergstraße in Germany on the scale of individual vineyard plots. The simulations showed that the risk for drought stress varies substantially between vineyard sites but might increase for steep-slope regions in the future. Possible adaptation measures depend highly on local conditions and are needed to make targeted use of water resources, while an intense interplay of different wine-industry stakeholders, research, knowledge transfer, and local authorities will be required.

Landslides are a major natural hazard, but uncertainties about their occurrence in a warmer climate are substantial. The relative role of rainfall, soil moisture, and land-use changes and the importance of climate change mitigation are not well understood. Here, we develop and apply a storyline approach to address these issues, considering a severe event from June 2009 in Austria with some 3000 landslides as showcase. The approach leverages on convection permitting simulations that realistically represent the meteorological event while sampling uncertainties. Depending on the changes of rainfall and soil moisture, the area affected during a 2009-type event could grow by 45% at 4 K global warming, although a slight reduction is also possible. Such growth could be reduced to less than 10% by limiting global warming according to the Paris agreement. Anticipated land-use changes towards a climate resilient forest would fully compensate for such a limited increase in hazard.

Heavy precipitation is a major natural hazard that can have severe impacts. In response to global warming, the character of heavy precipitation is expected to change. Projections of the future hydrologic cycle, especially of heavy precipitation, are uncertain. Especially at the regional scale, different data sources, such as different ensembles of global and regional climate models (GCMs and RCMs), provide sometimes conflicting conclusions. Therefore, it is even more important to investigate where differences between ensembles lie and to which processes they can be attributed.A precipitation scaling (introduced by Paul O’Gorman) is used to disentangle thermodynamic and dynamic contributions in extreme precipitation. In this work, we compare the results of CMIP5 and CMIP6 and focus on climate change signals between the periods 1971-2000 and 2071-2100 over Europe. The thermodynamic component provides homogeneous signals across Europe with a rise in extreme precipitation of about 7 %/K. In contrast, the dynamic component shows no spatial homogeneous results where the dynamic contribution can even modify the thermodynamic signal. The spread between the models within one ensemble is much larger. However, based on initial analyses, the spread in the CMIP6 models appears to have become smaller compared to CMIP5. This means, understanding the dynamic changes is the key to understanding the differences between the ensembles.As a next step, to analyze the discrepancy between CMIP5 and CMIP6 in terms of atmospheric circulation changes, we look into three atmospheric drivers: tropical and polar amplification of global warming and changes in stratospheric vortex strength.

Between 22-26 June 2009, Austria witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in southeast Styria, and property damages worth €10 Million in Styria itself. Elsewhere in Austria, flooding amounted to reparations worth €40 Million. Numerous synoptic-scale studies indicated the presence of a cut-off low over central Europe and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may manifest into a more intense event due to the higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation, more so in a topographically heterogeneous region such as the GAR (Greater Alpine Region).The flooding and landslides caused in the region raise an alarm and thus motivate this study whereby we investigate if the rainfall event did become stronger with time due to climate change compared to how it would have been in a counterfactual (climate change free) past. Here we have deployed the CCLM high-resolution regional model coupled with a statistical landslide model to simulate this event (rainfall and landslides) in a pseudo (surrogate) warming scenario. A marked decrease in rainfall intensity is observed in the simulations for 1° cooler climate (pre-industrial past) and the consequent landslide risk is reduced varying across GCMs that were used to derive the boundary conditions from.We discuss the results from the lens of attribution perspective - how conditional attribution is much more useful compared to the conventional risk-based approach of attributing extreme events. The novelty of our approach lies in using a high-resolution convection-permitting regional model for a landslide attribution study.

Long-duration dry spells in combination with temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a consecutive number of days with daily precipitation less than 1mm. CMIP5 models tend to underestimate the persistence of dry spells in Northern Europe while a large variability exists between model estimates in Central and Southern Europe where models have contrasting biases. Our results indicate that this variability in model estimates is due to inherent model differences and not internal variability. In Northern Europe, differences in the representation of persistent dry spells are related to the representation of persistent anticyclonic conditions. We also find a large spread in the representation of temperature extremes during dry spells. In Central and Southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Overall, there are large discrepancies in the representation of long-duration dry and hot events in the CMIP5 ensemble where the simulated climates vary from models with shorter-cooler dry spells to models with longer-hotter dry spells. This information is important to consider when interpreting the plausibility of future projections from climate models.

Human society and natural systems are intrinsically adapted to the local climate mean and variability. Therefore, changes relative to the local expected variability are highly relevant for assessing impact and planning for adaptation as the climate changes. We analyse the emerging climate signal relative to the diagnosed internal variability (signal-to-noise ratio, S/N) of a set of recently published climate indices over Europe. We calculate the signal-to-noise ratio with respect to a recent baseline (1951-1983) which relates to recent societal experience. In this framework, we find that during the 2000-2016 period, many areas of Europe already experienced significant changes in climate extremes, even when compared to this recent period which is within living memory. In particular, the S/N of extreme temperatures is larger than 1 and 2 over 34% and 4% of Europe, respectively. We also find that about 15% of Europe is experiencing more intense winter precipitation events, while in summer, 7% of Europe is experiencing stronger drought-inducing conditions.

Starkniederschläge sind eine große Naturgefahr, welche schwerwiegende Auswirkungen haben können. In Reaktion auf die globale Erwärmung wird erwartet, dass sich der Charakter von Starkniederschlägen ändern wird. Projektionen des zukünftigen Wasserkreislaufs, insbesondere von Starkniederschlägen, sind unsicher. Vor allem auf der regionalen Skala liefern verschiedene Datenquellen, wie verschiedene Ensembles von globalen und regionalen Klimamodellen (GCMs und RCMs), teils widersprüchliche Schlussfolgerungen. Daher ist es umso wichtiger, zu untersuchen wo Unterschiede zwischen Ensembles liegen und auf welche Prozesse diese zurückzuführen sind. Um zukünftige Änderungen von Niederschlägen über Europa zu ermitteln, werden saisonale Klimaänderungssignale von vier globalen und zwei regionalen Klimamodell-Ensembles betrachtet. Die verwendeten globalen Ensembles sind CMIP3, CMIP5, CMIP6 und HighResMIP; die regionalen Ensembles sind ENSEMBLES und EURO-CORDEX. Da unterschiedliche Emissionsszenarien bei den Projektionen der verschiedenen Ensembles verwendet wurden und um daher einen Vergleich der Ergebnisse zu gewährleisten, werden die Ergebnisse pro Grad Erwärmung (%/K) ausgedrückt. Wir betrachten 20 Jahres Wiederkehrwerte für Extremniederschläge. Für alle Jahreszeiten zeigen alle Ensembles eine Zunahme der Extremniederschläge. Um regionale Übereinstimmungen und Unterschiede zwischen den Ensembles zu erhalten, wird die paarweise Gaußsche Überlagerung betrachtet. Die Überlagerung aller Ensembles wird ausgedrückt durch das Mittel der paarweisen Ergebnisse. Die Ergebnisse vom Vergleich ENSEMBLES mit EURO-CORDEX, sowie CMIP5 mit CMIP6 zeigt überwiegend eine Übereinstimmung von mindestens 80% die jedoch lokal geringer ausfällt. Andere Kombinationen von Ensembles weisen mehr Unterschiede auf. Um die Unterschiede weiter zu untersuchen, wird ein Niederschlagsscaling verwendet, mit welchem die thermodynamischen und dynamischen Änderungssignale separat betrachtet werden können. Wir vergleichen CMIP5 mit CMIP6. Die thermodynamische Änderung über Europa zeigt homogen zunehmende Signale im Bereich von 7%/K. Im Unterschied zeigen die dynamischen Änderungen keine homogenen Signale. Auch der Spread zwischen den Modellen eines Ensembles ist für die dynamischen Änderungen bedeutend größer. Nach ersten Analysen scheint jedoch der Spread in den CMIP6 Modellen kleiner geworden zu sein verglichen mit CMIP5. Das heißt, die dynamischen Änderungen zu verstehen ist entscheidend, um die Unterschiede zwischen den Ensembles zu verstehen. Diese Analyse soll auch auf die EURO-CORDEX und CORDEX-FPS Ensembles ausgeweitet werden.

Extended periods without precipitation observed for example in Central Europe including Germany during the seasons from 2018 to 2020, can lead to water deficit and yield and quality losses for grape and wine production. However, irrigation infrastructure is largely non–existent. Regional climate models project changes of precipitation amounts and patterns, indicating an increase in frequency of occurrence of comparable situations in the future. In order to assess possible impacts of climate change on the water budget of grapevines, a water balance model was developed, which accounts for the large heterogeneity of vineyards with respect to their soil water storage capacity, evapotranspiration as a function of slope and aspect, and viticultural management practices. The model was fed with data from soil maps (soil type and plant available water capacity), a digital elevation model, the European Union (EU) vineyard–register, observed weather data and future weather data provided by regional climate models and a stochastic weather generator. This allowed conducting a risk assessment of the drought stress occurrence for the wine–producing regions Rheingau and Hessische Bergstraße in Germany on the scale of individual vineyard plots. The simulations showed that the risk for drought stress varies substantially between vineyard sites but might increase for steep–slope regions in the future. Possible adaptation measures depend highly on local conditions and to make targeted use of the resource water, an intense interplay of different wine-industry stakeholders, research, knowledge transfer, and local authorities will be required.

We analyze several sets of global and regional climate models (GCMs and RCMs) to investigate how robust climate change signals for seasonal mean and extreme precipitation are. The projections of the regional climate models ENSEMBLES and EURO-CORDEX are used along with projections of their driving global data sets of CMIP3 and CMIP5, respectively. In addition, projections of CMIP6 and the high-resolution HighResMIP global models are used. The projections are used with high emission scenarios (A1B or RCP8.5) depending on availability. To calculate the climate change signals a future period 2071-2100 and a baseline period 1971-2000 is chosen. For comparability and to reduce the uncertainty by the choice of the emission scenario, the climate change signals are normalized by the European mean surface temperature. We make statements of percentage change per degree warming. The analyses are carried out for eight European sub-regions: Alps, British Isles, Iberian Peninsula, France, Mid-Europe, Scandinavia, Mediterranean and Eastern Europe. We define extreme precipitation as the 20-year return values of each season. Regarding mean precipitation the climate change signals are robust across the different data sets. In accordance with previous studies, there is a transition zone between increasing and decreasing signals which is located in southern Europe in winter and more north in summer. This seasonal cycle can be found for all regions. For extreme precipitation, the climate change signals indicating increases in all seasons and regions. Especially in summer, in most regions the RCMs showing a higher increase compared to the GCMs up to a difference of about 5%/K for the ensemble medians. Hence, the signals for extremes are not that robust than for means.To understand where these differences come from, we are using a precipitation scaling for extremes to investigate the thermodynamic and dynamic contributions. The thermodynamic contribution shows homogeneous increasing signals for Europe. This means the dynamic contribution is the key to understand differences between the model ensembles.We aim to understand the discrepancy between different lines of evidence and focusing our study in the field of climate information distillation.

N. Ban et al., Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of ∼3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from ∼ −40% at 12 km to ∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales., Open access funding provided by University of Innsbruck and Medical University of Innsbruck., The ETH team, MZ, CNRM IPSL, ICTP, SMHI, Met-Office, DMI, CMCC, HZG, KNMI acknowledge funding from the HORIZON 2020 EUCP (European Climate Prediction System) project (https://www.eucp-project.eu, grant agreement No. 776613). AL-G acknowledges support by the Spanish government through grant BES-2016-078158 and MINECO/FEDER co-funded project MULTI-SDM (CGL2015-66583-R).

In June 2009 and September 2014, the Styrian Basin in Austria was affected by extreme events of heavy thunderstorms, triggering thousands of landslides. Since the relationship between intense rainfall, land cover/land use (LULC), and landslide occurrences is still not fully understood, our objective was to develop a model design that allows to assess landslide susceptibility specifically for past triggering events. We used generalized additive models (GAM) to link land surface, geology, meteorological, and LULC variables to observed slope failures. Accounting for the temporal variation in landslide triggering, we implemented an innovative spatio-temporal approach for landslide absence sampling. We assessed model performance using k-fold cross-validation in space and time to estimate the area under the receiver operating characteristic curve (AUROC). Furthermore, we analyzed the variable importance and its relationship to landslide occurrence. Our results showed that the models had on average acceptable to outstanding landslide discrimination capabilities (0.81&ndash, 0.94 mAUROC in space and 0.72&ndash, 0.95 mAUROC in time). Furthermore, meteorological and LULC variables were of great importance in explaining the landslide events (e.g., five-day rainfall 13.6&ndash, 17.8% mean decrease in deviance explained), confirming their usefulness in landslide event analysis. Based on the present findings, future studies may assess the potential of this approach for developing future storylines of slope instability based on climate and LULC scenarios.

During 22-24 June 2009, Austria witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in Feldbach, and property damages worth €10,000,000 in Styria itself. Numerous synoptic-scale studies indicated the presence of a cut-off low over the Adriatic and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may become more intense due to higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation.Precipitation, as a natural atmospheric phenomenon, is dependent upon the dynamic and thermodynamic characteristics of the atmosphere. While it is safe to say that the thermodynamic characteristics of the atmosphere are relatively easier to simulate with confidence using available global models, the same cannot be said about the dynamics. This can be blamed on the chaotic non-linear behaviour of the atmosphere and problem in resolving sub-grid scale processes that reduce the model accuracy for longer spatial scales.CCLM regional model is used to study this extreme precipitation event. Our setup uses IFS data to calculate initial and boundary conditions for the simulations of the ‘present’ case where our attempt is to recreate the event over the same location as the original event. Further we use CMIP5 global climate models (at the RCP8.5) scenario. In particular, these will be applied in the ‘surrogate climate change’ method. Here, the climate change signals are calculated by computing the difference between the thermodynamic fields of the CMIP5 simulations for the future and the past. These climate change signals are applied to the original fields to obtain the ‘changed’ fields which are used to calculate new initial and boundary conditions resembling a climate-change future. A similar approach is to be applied for the ‘past’ case simulations.The idea behind this experimental setup is to establish a ‘storyline’ for the event as it would have occurred in the past, present and the future. The storyline approach provides an alternative to the traditional probabilistic approach for assessing risk enhancement and can serve to study responses of different mechanisms to climate change. The storyline approach also helps in decision-making as event-oriented risk management is easy for people to perceive and respond to. An associated landslide modelling study, which uses the precipitation output of our simulations as input, looks into the probable increased risks of landslides in the region and will directly aid the lives of those living in Southeast Austria.

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many bias adjustment methods, which merely correct for deficiencies in the distribution, have been developed. Despite adjusting the desired features under historical simulations, their application in a climate change context is subject to additional uncertainties and modifications of the change signals, especially for climate indices which have not been tackled by the methods. In this sense, some of the commonly-used bias adjustment methods allow changes of the signals, which appear by construction in case of intensity-dependent biases; some others ensure the trends in some statistics of the original, raw models. Two relevant sources of uncertainty, often overlooked, which bring further uncertainties are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect).In the present work, we assess the impact of these factors on the climate change signal of a set of climate indices of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature -empirical or parametric-, fitted parameters and preservation of the signals) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping -QDM-, scaled distribution mapping -SDM- and the method from the third phase of ISIMIP -ISIMIP3) preserve better the raw signals for the different indices and variables (not all preserved by construction). However they rely largely on the reference dataset used for calibration, thus present a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20km) and low (approximately 120km) spatial resolutions.

Weather- and climate-related extreme events such as droughts, heatwaves and storms arise from interactions between complex sets of physical processes across multiple spatial and temporal scales, often overwhelming the capacity of natural and/or human systems to cope. In many cases, the greatest impacts arise through the ‘compounding’ effect of weather and climate-related drivers and/or hazards, where the scale of the impacts can be much greater than if any of the drivers or hazards occur in isolation; for instance, when a heavy precipitation falls on an already saturated soil causing a devastating flood. Compounding in this context refers to the amplification of an impact due to the occurrence of multiple drivers and/or hazards either because multiple hazards occur at the same time, previous climate conditions or weather events have increased a system’s vulnerability to a successive event, or spatially concurrent hazards lead to a regionally or globally integrated impact. More generally, compound weather and climate events refer to a combination of multiple climate drivers and/or hazards that contributes to societal or environmental risk.Although many climate-related disasters are caused by compound events, our ability to understand, analyse and project these events and interactions between their drivers is still in its infancy. Here we review the current state of knowledge on compound events and propose a typology to synthesize the available literature and guide future research. We organize the highly diverse event types broadly along four main themes, namely preconditioned, multivariate, temporally compounding, and spatially compounding events. We highlight promising analytical approaches tailored to the different event types, which will aid future research and pave the way to a coherent framework for compound event analysis. We further illustrate how human-induced climate change affects different aspects of compound events, such as their frequency and intensity through variations in the mean, variability, and the dependence between their climatic drivers. Finally, we discuss the emergence of new types of events that may become highly relevant in a warmer climate.

This paper is a contribution to the special issue on Advances in Convection-Permitting Climate Modeling, consisting of papers that focus on the evaluation, climate change assessment, and feedback processes in kilometer-scale simulations and observations., A recently launched project under the auspices of the World Climate Research Program’s (WCRP) Coordinated Regional Downscaling Experiments Flagship Pilot Studies program (CORDEX-FPS) is presented. This initiative aims to build first-of-its-kind ensemble climate experiments of convection permitting models to investigate present and future convective processes and related extremes over Europe and the Mediterranean. In this manuscript the rationale, scientific aims and approaches are presented along with some preliminary results from the testing phase of the project. Three test cases were selected in order to obtain a first look at the ensemble performance. The test cases covered a summertime extreme precipitation event over Austria, a fall Foehn event over the Swiss Alps and an intensively documented fall event along the Mediterranean coast. The test cases were run in both “weather-like” (WL, initialized just before the event in question) and “climate” (CM, initialized 1 month before the event) modes. Ensembles of 18–21 members, representing six different modeling systems with different physics and modelling chain options, was generated for the test cases (27 modeling teams have committed to perform the longer climate simulations). Results indicate that, when run in WL mode, the ensemble captures all three events quite well with ensemble correlation skill scores of 0.67, 0.82 and 0.91. They suggest that the more the event is driven by large-scale conditions, the closer the agreement between the ensemble members. Even in climate mode the large-scale driven events over the Swiss Alps and the Mediterranean coasts are still captured (ensemble correlation skill scores of 0.90 and 0.62, respectively), but the inter-model spread increases as expected. In the case over Mediterranean the effects of local-scale interactions between flow and orography and land–ocean contrasts are readily apparent. However, there is a much larger, though not surprising, increase in the spread for the Austrian event, which was weakly forced by the large-scale flow. Though the ensemble correlation skill score is still quite high (0.80). The preliminary results illustrate both the promise and the challenges that convection permitting modeling faces and make a strong argument for an ensemble-based approach to investigating high impact convective processes., IG and LS have been supported by the Croatian Science Foundation (HrZZ) project CARE (no. 2831). JF acknowledges support by the Spanish R + D programme through MINECO/FEDER co-funded project INSIGNIA (CGL2016-79210-R). AL-G is supported by the Spanish government though grant BES-2016-078158 and MINECO/FEDER co-funded project MULTI-SDM (CGL2015-66583-R). JM and KW-S gratefully acknowledge the support by the German Science Foundation (DFG) through project FOR 1695. DM, MP, and HT gratefully acknowledge the support received via the Austrian Science Fund (FWF) project NHCM-2 (no. P24758-N29) and the projects HighEnd:Extremes and EASICLIM, funded by the Austrian Climate Research Programme (ACRP) of the Klima- und Energiefonds (nos. KR13AC6K10981 and KR16AC0K13160, respectively). SS and TL acknowledge the support of NOTUR project no. NN9280K and the Research Council of Norway and its basic institute support of their strategic project on Climate Services. RMC and PMMS gratefully acknowledge the support of the SOLAR project (PTDC/GEOMET/7078/2014) financed by the Portuguese Foundation for Science and Technology. EJ Kendon gratefully acknowledges funding from the Joint Department of Energy and Climate Change (DECC) and Department for Environment Food and Rural Affairs (Defra) Met Office Hadley Centre Climate Programme (GA01101). S. Khodayar research is supported by the Bundesministerium für Bildung und Forschung (BMBF; German Federal Ministry of Education and Research). Support of Ministry of Education, Youth and Sports of the Czech Republic for funding the participation in Euro-CORDEX activities via the scheme INTER-TRANSFER under the Grant no. LTT17007. Funding from the Research Council of Norway through the SUPER (Grant no. 250573) and HYPRE (Grant no. 243942) projects.

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions., Participation of S. Herrera and J.M. Gutiérrez was partially supported by the project AfriCultuReS (European Union's Horizon 2020 program, grant agreement no, 774652). S. Lange acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 641816 (CRESCENDO).

Compound flooding arises from storms causing concurrent extreme meteorological tides (that is the superposition of storm surge and waves) and precipitation. This flooding can severely affect densely populated low-lying coastal areas. Here, combining output from climate and ocean models, we analyse the concurrence probability of the meteorological conditions driving compound flooding. We show that, under a high emissions scenario, the concurrence probability would increase globally by more than 25% by 2100 compared to present. In latitudes above 40o north, compound flooding could become more than 2.5 times as frequent, in contrast to parts of the subtropics where it would weaken. Changes in extreme precipitation and meteorological tides account for most (77% and 20%, respectively) of the projected change in concurrence probability. The evolution of the dependence between precipitation and meteorological tide dominates the uncertainty in the projections. Our results indicate that not accounting for these effects in adaptation planning could leave coastal communities insufficiently protected against flooding., Concurrent coastal extremes - storm surges and flooding from precipitation - are 2.5 times as frequent in latitudes higher than 40∘N under a high emission scenario by 2100 compared to today, according to an analysis of climate and ocean model output.

VALUE is a network that developed a framework to evaluate statistical downscaling methods including model output statistics such as simple bias correction and quantile mapping; perfect prognosis methods such as regression models and analog methods; and weather generators. The first experiment addresses the downscaling performance in present climate with perfect predictors. This paper presents a synthesis of the VALUE special issue, with a focus on the results of this first experiment. This paper presents a synthesis of the results. Model output statistics performs mostly well, but requires predictors at a resolution close to the target one. Perfect prog performance depends crucially on model structure and predictor choice. Weather generators perform in principle well for all aspects that can be expressed by the available model structure. Inter-annual variability is underrepresented by both perfect prog and weather generator approaches. Spatial variability is poorly represented by almost all participating methods (inherited by model output statistics from the driving model, not represented by the perfect prog and weather generator methods). Further studies are required to systematically assess (a) the role of predictor choice for perfect prog; (b) the performance of spatial weather generators, to study the performance based on GCM predictors; (c) downscaling skill in simulated future climates; and (d) the credibility of simulated predictors in a future climate., VALUE was funded from 2012 to 2015 as EU COST Action ES1102.

We demonstrate both analytically and with a modelling example that cross-validation of free-running bias-corrected climate change simulations against observations is misleading. The underlying reasoning is as follows: a cross-validation can have in principle two outcomes. A negative (in the sense of not rejecting a null hypothesis), if the residual bias in the validation period after bias correction vanishes; and a positive, if the residual bias in the validation period after bias correction is large. It can be shown analytically that the residual bias depends solely on the difference between the simulated and observed change between calibration and validation periods. This change, however, depends mainly on the realizations of internal variability in the observations and climate model. As a consequence, the outcome of a cross-validation is also dominated by internal variability, and does not allow for any conclusion about the sensibility of a bias correction. In particular, a sensible bias correction may be rejected (false positive) and a non-sensible bias correction may be accepted (false negative). We therefore propose to avoid cross-validation when evaluating bias correction of free-running bias-corrected climate change simulations against observations. Instead, one should evaluate non-calibrated temporal, spatial and process-based aspects.

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases. Bias correction methods aim to remove introduced bias for climate model simulations; however, improper use can result in spurious climate signals. This Perspective considers the issues of bias correction and makes recommendations for research to overcome model biases.

Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short-term variations, spells and variability from interannual to long-term trends. The EU-COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long-term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors., EU COST Association, Grant/Award Number: ES1102; Ministry of Education, Youth, and Sports of the Czech Republic, Grant/AwardNumber:LD12029

More intense precipitation will increase the flood potential from concurring storm surges and precipitation in the future., In low-lying coastal areas, the co-occurrence of high sea level and precipitation resulting in large runoff may cause compound flooding (CF). When the two hazards interact, the resulting impact can be worse than when they occur individually. Both storm surges and heavy precipitation, as well as their interplay, are likely to change in response to global warming. Despite the CF relevance, a comprehensive hazard assessment beyond individual locations is missing, and no studies have examined CF in the future. Analyzing co-occurring high sea level and heavy precipitation in Europe, we show that the Mediterranean coasts are experiencing the highest CF probability in the present. However, future climate projections show emerging high CF probability along parts of the northern European coast. In several European regions, CF should be considered as a potential hazard aggravating the risk caused by mean sea level rise in the future.

The propagation of drought from meteorological drought to soil moisture drought can be accelerated by high temperatures during dry periods. The occurrence of extremely long-duration dry periods in combination with extremely high temperatures may drive larger soil moisture deficits than either extreme occurring alone, and lead to severe impacts. In this study, we propose a framework to both characterise long-duration meteorological droughts that co-occur with extremely high temperatures and quantify their probability. We term these events as long-duration, dry and hot (DH) events and characterise them by their duration (D) and magnitude (M). D is defined as the consecutive number of days with precipitation below 1 mm, while M is the maximum daily maximum temperature during an event. A copula-based approach is then employed to estimate the probability of DH events. The framework is applied to Europe during the summer months of June, July and August. We also assess the change in probability that has occurred over the historical period 1950–2013 and find an increased probability of DH events throughout Europe where rising temperatures are found to be the main driver of this change. Dry periods are becoming hotter, leading to an increase in the occurrence of long-duration dry periods with extremely high temperatures. Some parts of Europe also show an increased probability of long-duration events although the relative change is not as strong as that seen with temperature. The results point to a predominant thermodynamic response of DH events to global warming and reaffirm previous research that soil moisture drought events are setting in faster and becoming more severe due to a change in the contributing meteorological hazards. It is hoped that the framework applied here will provide a starting point for further analysis of DH events in other locations and for the assessment of climate models.

Special Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research., Credible information about the properties and changes of extreme events on the regional and local scales is of prime importance in the context of future climate change. Within the EU-COST Action VALUE a comprehensive validation framework for downscaling methods has been developed. Here we present validation results for extremes of temperature and precipitation from the perfect predictor experiment that uses reanalysis-based predictors to isolate downscaling skill. The raw reanalysis output reveals that there is mostly a large bias with respect to the extreme index values at the considered stations across Europe, clearly pointing to the necessity of downscaling. The performance of the downscaling methods is closely linked to their specific structure and setup. All methods using parametric distributions require non-standard distributions to correctly represent marginal aspects of extremes. Also, the performance is much improved by explicitly including a seasonal component, particularly in case of precipitation. With respect to the marginal aspects of extremes the best performance is found for model output statistics (MOS), weather generators (WGs) as well as perfect prognosis (PP) methods using analogues. Spell-length-related extremes of temperature are best assessed by MOS and WGs, spell-length-related extremes of precipitation by MOS and PP methods using analogues. The skill of PP methods with transfer functions varies strongly across the methods and depends on the extreme index, region and season considered., This work has been carried out as part of the EU‐COST Action VALUE (ES1102). The authors are particularly grateful for the instructive and very helpful comments of two anonymous reviewers. P.M.M.S. thanks the Portuguese Foundation for Science and Technology (FCT) for funding under Project SOLAR‐PTDC/GEOMET/7078/2014.

Glaciers are of key importance to freshwater supplies in the Himalayan region. Their growth or decline is among other factors determined by an interaction of 2‐m air temperature (TAS) and precipitation rate (PR) and thereof derived positive degree days (PDD) and snow and ice accumulation (SAC). To investigate determining factors in climate projections, we use a model ensemble consisting of 36 CMIP5 general circulation models (GCMs) and 13 regional climate models (RCMs) of two Asian CORDEX domains for two different representative concentration pathways (RCP4.5 and RCP8.5). First, we downsize the ensemble in respect to the models' ability to correctly reproduce dominant circulation patterns (i.e., the Indian summer monsoon [ISM] and western disturbances [WDs]) as well as elevation‐dependent trend signals in winter. Within this evaluation, a newly produced data set for the Indus, Ganges and Brahmaputra catchments is used as observational data. The reanalyses WFDEI, ERA‐Interim, NCEP/NCAR and JRA‐55 are used to further account for observational uncertainty. In a next step, remaining TAS and PR data are bias corrected applying a new bias adjustment method, scale distribution mapping, and subsequently PDD and SAC computed. Finally, we identify and quantify projected climate change effects. Until the end of the century, the ensemble indicates a rise of PDD, especially during summer and for lower altitudes. Also TAS is rising, though the highest increases are shown for higher altitudes and between December and April (DJFMA). PRs connected to the ISM are projected to robustly increase, while signals for PR changes during DJFMA show a higher level of uncertainty and spatial heterogeneity. However, a robust decline in solid precipitation is projected over our research domain, with the exception of a small area in the high mountain Indus catchment where no clear signal emerges., Until the end of the century, the ensemble indicates a rise of positive degree days, especially during summer and for lower altitudes. Precipitation (PR) connected to the Indian summer monsoon are projected to robustly increase, while signals for PR changes during DJFMA show a higher level of uncertainty and spatial heterogeneity. Solid precipitation is robustly projected to decline except for some areas in the high mountain Indus catchment. Changes in the annual cycle of positive degree days (PDD, top row) and snow and ice accumulation (SAC, bottom row) for RCP4.5 (first and third column) and RCP8.5 (second and fourth column) until the end of the century (1981–2010 to 2071–2100). The black line indicates the ensemble mean. Dashed lines show the 25th and 75th percentile (grey shaded area), dotted lines show the 5th and 95th percentile (light‐grey shaded area). The number in the top right corner of each plot indicates the number of included climate models.

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

This work analyses three uncertainty sources affecting the observation-based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44°), three interpolation methods, both areal- and point-representative implementations, and three different densities of the underlying station network (high/medium/low density). As a result, for each resolution and interpolation approach, nine different grids have been obtained for each country and inter-compared using a variance decomposition methodology. Results indicate larger differences among the data sets for Spain than for Poland, mainly due to the larger spatial variability and complex orography of the former region. The variance decomposition points out to station density as the most influential factor, independent of the season, the areal- or point-representative implementation and the country considered, and slightly increasing with the spatial resolution. In contrast, the decomposition is stable when extreme precipitation indices are considered, in particular for the 50-year return value. Finally, the uncertainty due to station sub-sampling inside a particular grid box decreases with the number of stations used in the averaging/interpolation. In the case of spatially homogeneous grid boxes, the interpolation approach obtains similar results for all the parameters, excepting the wet day frequency, independently of the number of stations. When there is a more significant internal variability in the grid box, the interpolation is more sensitive to the number of stations, pointing out to a minimum stations’ density for the target resolution (six to seven stations)., VALUE has been funded as EU COST Action ES1102. Participation of S.H. and J.M.G. was partially supported by the project MULTI‐SDM (CGL2015‐66583‐R, MINECO/FEDER). P.M.M.S. and R.M.C. wish to acknowledge the projects SOLAR (PTDC/GEOMET/7078/2014) and FCT UID/GEO/50019/ 2013 ‐ Instituto Dom Luiz, both financed by the Fundação para a Ciência e Tecnologia.

Special Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research: et al., VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process-based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis-driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method-to-method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO-CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal., This work has been performed in the framework of the VALUE COST Action ES1102, under FP7 programme J.M.G. and S.H. acknowledge partial funding from MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). B.H. and D.R. acknowledge COMPLEX project (FP7-ENV-2012, No: 308601). M.T. was supported by HOPE project (MINECO, CGL2014-52571-R). Participation of M.D., P.S. and R.H. was funded by the Ministry of Education, Youth, and Sports of the Czech Republic contracts LD12029, LD14043 and LD12059, respectively.

The spatial dependence of meteorological variables is crucial for many impacts, for example, droughts, floods, river flows, energy demand, and crop yield. There is thus a need to understand how well it is represented in downscaling (DS) products. Within the COST Action VALUE, we have conducted a comprehensive analysis of spatial variability in the output of over 40 different DS methods in a perfect predictor setup. The DS output is evaluated against daily precipitation and temperature observations for the period 1979–2008 at 86 sites across Europe and 53 sites across Germany. We have analysed the dependency of correlations of daily temperature and precipitation series at station pairs on the distance between the stations. For the European data set, we have also investigated the complexity of the downscaled data by calculating the number of independent spatial degrees of freedom. For daily precipitation at the German network, we have additionally evaluated the dependency of the joint exceedance of the wet day threshold and of the local 90th percentile on the distance between the stations. Finally, we have investigated regional patterns of European monthly precipitation obtained from rotated principal component analysis. We analysed Perfect Prog (PP) methods, which are based on statistical relationships derived from observations, as well as Model Output Statistics (MOS) approaches, which attempt to correct simulated variables. In summary, we found that most PP DS methods, with the exception of multisite analog methods and a method that explicitly models spatial dependence yield unrealistic spatial characteristics. Regional climate model-based MOS methods showed good performance with respect to correlation lengths and the joint occurrence of wet days, but a substantial overestimation of the joint occurrence of heavy precipitation events. These findings apply to the spatial scales that are resolved by our observation network, and similar studies with higher resolutions, which are relevant for small hydrological catchment, are desirable., EU, Grant/Award Number: EU COST Action ES1102.

MV acknowledges funding from French National Research Agency (ANR): ANR-project StaRMIP (grant agreement ANR-12-JS06-0005-01). SMcG acknowledges contributions from U.S. Department of Defense ESTCP Grant: RC-201666 and U.S. Department of Energy RGCM Grant: DE-SC0016438.

Climate models are our major source of knowledge about climate change. The impacts of climate change are often quantified by impact models. Whereas impact models typically require high resolution unbiased input data, global and regional climate models are in general biased, their resolution is often lower than desired. Thus, many users of climate model data apply some form of bias correction and downscaling. A fundamental assumption of bias correction is that the considered climate model produces skillful input for a bias correction, including a plausible representation of climate change. Current bias correction methods cannot plausibly correct climate change trends, and have limited ability to downscale. Cross validation of marginal aspects is not sufficient to evaluate bias correction and needs to be complemented by further analyses. Future research should address the development of stochastic models for downscaling and approaches to explicitly incorporate process understanding.

This paper deals with the subject of elaborating a guideline on the use of climate data, which have been made available to the climate-change and climate-impact community as well as to the general public. The therein descripted data is a gridded dataset on 1 km nominal resolution for Austria, consisting of observational data as well as of bias corrected climate model output. It covers the area of Austria. The aim of this paper is to make reference to this guideline that is by now accessible for those interested in using the dataset as well as for those creating datasets planning to provide guidance to their data users.

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a ‘storyline’ approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change.

The published version of this article is available at https://advances.sciencemag.org/content/5/9/eaaw5531. Compound flooding (CF) is an extreme event taking place in low-lying coastal areas as a result of co-occurring high sea level and large amounts of runoff, caused by precipitation. The impact from the two hazards occurring individually can be significantly lower than the result of their interaction. Both the risk of storm surges and heavy precipitation, as well as their interplay is likely to change in response to anthropogenic global warming. Despite CF relevance, a comprehensive risk assessment beyond individual locations at the country scale is missing. In particular, no studies have examined possible future CF risk. Here we estimate the potential CF risk along the European coasts both for present and future climate according to the business-as-usual (RCP8.5) scenario. Under current climate conditions, the locations experiencing the highest risk are mostly located along the Mediterranean Sea. However, future climate projections show emerging risk along parts of the Atlantic coast and the North Sea. The increase of the risk is mostly driven by an intensification of precipitation extremes. In several European regions, increasing CF risk should be considered as a potential hazard aggravating the risk caused by the mean sea level rise (SLR).