[1] Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of spacetime variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

ABSTRACT:Although regional climate models (RCMs) are powerful tools for describing regional and even smaller scale climate conditions, they still feature severe systematic errors. In order to provide optimized climate scenarios for climate change impact research, this study merges linear and nonlinear empirical-statistical downscaling techniques with bias correction methods and investigates their ability for reducing RCM error characteristics. An ensemble of seven empiricalstatistical downscaling and error correction methods (DECMs) is applied to post-process daily precipitation sums of a high-resolution regional climate hindcast simulation over the Alpine region, their error characteristics are analysed and compared to the raw RCM results.Drastic reductions in error characteristics due to application of DECMs are demonstrated. Direct point-wise methods like quantile mapping and local intensity scaling as well as indirect spatial methods as nonlinear analogue methods yield systematic improvements in median, variance, frequency, intensity and extremes of daily precipitation. Multiple linear regression methods, even if optimized by predictor selection, transformation and randomization, exhibit significant shortcomings for modelling daily precipitation due to their linear framework. Comparing the well-performing methods to each other, quantile mapping shows the best performance, particularly at high quantiles, which is advantageous for applications related to extreme precipitation events. The improvements are obtained regardless of season and region, which indicates the potential transferability of these methods to other regions.

Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors, its ability to generate "new extremes" (values outside the calibration range), and its impact on the climate change signal (CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation of the error correction function enables QM to reproduce "new extremes" without deterioration and mostly with improvement of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly derived indices of extremes, such as threshold indices.

Electricity generated by hydro power is the most widely used form of renewable energy, and as such, its vulnerability to climate change is of great interest. The aim of this work is to estimate the change in river discharge characteristics in the Alpine region due to possible impacts of climate and the related changes in the power generation of run-of-river hydro power plants up to 2050. Four representative bias-corrected climate simulations from the ENSEMBLES project are chosen based on the SRES greenhouse gas emission scenario pathway A1B. Data of these simulations serve as input for a lumped-parameter rainfall-runoff model at a monthly time step, which is calibrated on discharge data of gauging stations along important rivers in the Alpine region. A power plant model fed with runoff data generated by the hydrological model is used to compute changes in the long-term average annual net electrical energy output of hydro power plants for the whole Alpine region; while the model for Austria is based on known technical parameters of the power plants, a more simplified approach is employed elsewhere. The general warming trend observed in all four climate scenarios causes to various degrees a seasonal shift towards earlier runoff. However, more diverse changes in precipitation for the different climate scenarios and time periods result in diverging hydrological projections. Although the annual runoff is found to decrease in some scenarios, the generally observed shift of runoff towards the winter season that typically shows higher energy consumption in the Alpine region suggests that the overall impact for the electricity sector tends to be positive rather than negative. Estimated changes in the average annual electricity generation of runof-river plants are generally found to be within a singledigit percentage range but can be either positive or negative depending on the climate scenario. The estimated ranges reflect the diversity (uncertainty) of the climate models; the total bandwidth of possible changes in the water availability and hydro power generation in the Alpine region up to 2050 is assumed to be even higher, because of other uncertainties in the model chain that are not explicitly considered here. Nevertheless, as the general regional trends and bandwidth of changes in runoff and hydro power production strongly depend on the future changes in precipitation, the results of this work provide reasonable orders of magnitude of expected changes and are seen as a first step towards an improved understanding of climate impacts on hydro power production within the entire Alpine region.

Abstract. This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decisionmakers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VI-ACS Advisory Board also recommended an impacts versionPublished by Copernicus Publications on behalf of the European Geosciences Union.

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Climate change affects regions differently and therefore also climate change effects on energy systems need to be analyzed region specific. The objective of the study presented is to show and analyze these effects on regional energy systems following a high spatial resolution approach. Three regional climate scenarios are downscaled to a 1 km resolution and error corrected for three different testing regions in Austria. These climate data are used to analyze effects of climate change on heating and cooling demand until the year 2050. Potentials of renewable energies such as solar thermal, photovoltaic, ambient heat and biomass are also examined. In the last process step the outcomes of the previous calculations are fed into two energy system models, where energy system optimizations are executed, which provide information concerning optimal setups and operations of future energy systems. Due to changing climate strong changes for the energy demand structure are noticed; lower heat demand in winter (between -7 and -15% until 2050) and - strongly differing between regions - higher cooling demand in summer (up to +355%). Optimization results show that the composition of energy supply carriers is barely affected by climate change, since other developments such as refurbishment actions, price developments and regional biomass availabilities are more influencing within this context.