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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.

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.

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.

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.

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.

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.

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).

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.

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

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.

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.

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).

Climate model resolution can affect both the climate change signal and present-day representation of extreme precipitation. The need to parametrize convective processes raises questions about how well the response to warming of convective precipitation extremes is captured in such models. In particular, coastal precipitation extremes can be sensitive to sea surface temperature (SST) increase. Taking a recent coastal precipitation extreme as a showcase example, we explore the added value of convection-permitting models by comparing the response of the extreme precipitation to a wide range of SST forcings in an ensemble of regional climate model simulations using parametrized and explicit convection. Compared at the same spatial scale, we find that the increased local intensities of vertical motion and precipitation in the convection-permitting simulations play a crucial role in shaping a strongly nonlinear extreme precipitation response to SST increase, which is not evident when convection is parametrized. In the convection-permitting simulations, SST increase causes precipitation intensity to increase only until a threshold is reached, beyond which further SST increase does not enhance the precipitation. This flattened response results from an improved representation of convective downdrafts and near-surface cooling, which damp the further intensification of precipitation by stabilizing the lower troposphere locally and also create cold-pools that cause subsequent convection to be triggered at sea, rather than by the coastal orography. These features are not well represented in the parametrized convection simulations, resulting in precipitation intensity having a much more linear response to increasing SSTs

To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. For most of Europe, this choice is well justified. But, based on regional climate simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of mountain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong air masses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data.

Over the past 60 years, both average daily precipitation intensity and extreme precipitation have increased in many regions1, 2, 3. Part of these changes, or even individual events4, 5, have been attributed to anthropogenic warming6, 7. Over the Black Sea and Mediterranean region, the potential for extreme summertime convective precipitation has grown8 alongside substantial sea surface temperature increase. A particularly devastating convective event experienced in that region was the July 2012 precipitation extreme near the Black Sea town of Krymsk9. Here we study the effect of sea surface temperature (SST) increase on convective extremes within the region, taking the Krymsk event as a showcase example. We carry out ensemble sensitivity simulations with a convection-permitting atmospheric model and show the crucial role of SST increase in the extremeness of the event. The enhancement of lower tropospheric instability due to the current warmer Black Sea allows deep convection to be triggered, increasing simulated precipitation by more than 300% relative to simulations with SSTs characteristic of the early 1980s. A highly nonlinear precipitation response to incremental SST increase suggests that the Black Sea has exceeded a regional threshold for the intensification of convective extremes. The physical mechanism we identify indicates that Black Sea and Mediterranean coastal regions may face abrupt amplifications of convective precipitation under continued SST increase, and illustrates the limitations of thermodynamical bounds for estimating the temperature scaling of convective extremes.

We investigate how well a suite of regional climate models (RCMs) from the ENSEMBLES project represents the residual spatial dependence of daily precipitation. The study area we consider is a 200 km×200 km region in south central Norway, with RCMs driven by ERA-40 boundary conditions at a horizontal resolution of approximately 25 km×25 km. We model the residual spatial dependence with pair-copula constructions, which allows us to assess both the overall and tail dependence in precipitation, including uncertainty estimates. The selected RCMs reproduce the overall dependence rather well, though the discrepancies compared to observations are substantial. All models overestimate the overall dependence in the west-east direction. They also overestimate the upper tail dependence in the north-south direction during winter, and in the west-east direction during summer, whereas they tend to underestimate this dependence in the north-south direction in summer. Moreover, many of the climate models do not simulate the small-scale dependence patterns caused by the pronounced orography well. However, the misrepresented residual spatial dependence does not seem to affect estimates of high quantiles of extreme precipitation aggregated over a few grid boxes. The underestimation of the area-aggregated extreme precipitation is due mainly to the well-known underestimation of the univariate margins for individual grid boxes, suggesting that the correction of RCM biases in precipitation might be feasible

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License.-- et al., VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies., VALUE is funded via the EU FP7 programme as EU COST Action ES1102.

Downscaling in Weather Forecasting The first downscaling methods had been invented already in the late 1940s (Klein 1948) and became operational in the early days of numerical weather prediction at the end of the 1950s. Back then, operational numerical weather prediction models were by far too coarse to predict local weather, and furthermore they did not forecast all variables of interest but only a few such as pressure and temperature. At that time a considerable network of observed weather time series was available already. Klein et al. (1959) employed this data to infer statistical relationships between the observed large-scale circulation – for those variables that were simulated by the models – and the observed local-scale weather variables of interest. The statistical model was then applied to downscale the actual numerical forecast of the large-scale circulation to a forecast of the local weather. The key assumption of this approach is that the large-scale predictor has been perfectly forecasted by the numerical model, hence the approach itself has been coined perfect prognosis (PP). After some years, a considerable database of past forecasts had been archived. Analyses of this data revealed that numerical forecasts even of the largescale weather were of course not perfect but showed systematic deviations compared to observations. Yet this database also became key to mitigate this problem: Glahn and Lowry (1972) developed a new approach that – during calibration – did not take the predictors from observations but from the archived numerical forecasts. For a new weather prediction, the inferred statistical link is then applied to the new numerical forecast. As this approach is basically a post-processing of numerical model data, it has been coined model output statistics (MOS). The key advantage of MOS is that it contains by construction a bias correction of the numerical model. Current weather prediction systems employ complex MOS approaches with several predictors that are continually recalibrated to provide the highest predictive skill. In parallel, numerical approaches were developed to improve the resolution and accuracy of forecasts over a target region. The first limited-area model was developed at the US National Meteorological Center (Howcroft 1966, Gerrity and McPherson 1969) and became operational in 1971. This model covered the US, Canada and the Arctic Ocean at a horizontal resolution of 190.5km at 60◦N and was driven at the lateral boundaries with input from a Northern Hemisphere numerical weather prediction model.

Compound events are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint – dependent – occurrence causes an extreme impact. The conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present day and future climate, as well as the uncertainty estimates around such risk. The model includes meteorological predictors which provide insight into both the involved physical processes, and the temporal variability of CEs. Moreover, this model provides multivariate statistical downscaling of compound events. Downscaling of compound events is required to extend their risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events, or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk, in particular the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.