[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.

The analysis of possible regional climate changes over Europe as simulated by ten regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models.Two fundamental aspects of model validation are addressed here: the ability to simulate i) the longterm (30 or 40 years) mean climate and ii) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer.In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.4

The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II. This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.

There are two main uncertainties in determining future climate: the trajectories of future emissions of greenhouse gases and aerosols, and the response of the global climate system to any given set of future emissions [Meehl et al., 2007]. These uncertainties normally are elucidated via application of global climate models, which provide information at relatively coarse spatial resolutions. Greater interest in, and concern about, the details of climate change at regional scales has provided the motivation for the application of regional climate models, which introduces additional uncertainty [Christensen et al., 2007a]. These uncertainties in fine‐scale regional climate responses, in contrast to uncertainties of coarser spatial resolution global models in which regional models are nested, now have been documented in numerous contexts [Christensen et al., 2007a] and have been found to extend to uncertainties in climate impacts [Wood et al., 2004; Oleson et al., 2007]. While European research in future climate projections has moved forward systematically to examine combined uncertainties from global and regional models [Christensen et al., 2007b], North American climate programs have lagged behind.

Using the PRECIS regional climate modeling system this study analyses the distribution of extremes of temperature and precipitation in South America in the recent past and in a future (2071-2100) climate under the IPCC SRES A2 and B2 emissions scenarios. The results show that for the present climate the model simulates well the spatial distribution of extreme temperature and rainfall events when compared with observations, with temperature the more realistic. The observations over the region are far from comprehensive which compromises the assessment of model quality. In all the future climate scenarios considered all parts of the region would experience significant and often different changes in rainfall and temperature extremes. In the future, the occurrence of warm nights is projected to be more frequent in the entire tropical South America while the occurrence of cold night events is likely to decrease. Significant changes in rainfall extremes and dry spells are also projected. These include increased intensity of extreme precipitation events over most of Southeastern South America and western Amazonia consistent with projected increasing trends in total rainfall in these regions. In Northeast Brazil and eastern Amazonia smaller or no changes are seen in projected rainfall intensity though significant changes are seen in the frequency of consecutive dry days.

SUMMARYPresent-day climate simulations for Europe are presented, based on a SO km regional-climate model (RCM) driven by output from a global general-circulation model (GCM) using a one-way nesting approach. Both models are components of the Meteorological Office Unified Forecast/Climate Model and use the same subgrid-scale physics. The relationship between the RCM circulation and that of the driving GCM was assessed in seasonal RCM integrations using domains of different sizes. In the larger domains, both the mean flow and the day-to-day variability in the RCM diverge from that of the GCM on the synoptic scale, rendering the RCM solution physically inconsistent with the GCM solution external to the RCM domain. At the grid-point scale the RCM freely generates its own features, even in the smaller domains-only at points adjacent to the boundary buffer zone is there evidence of significant distortion by the lateral boundary forcing from the GCM.Using one of the smaller domains, a 10-year RCM simulation was carried out, driven by a coupled atmosphere/ mixed-layer-ocean version of the GCM. Over the region of interest the general circulation and daily synoptic variability is realistically simulated by the GCM and, therefore, also by the RCM (see above). Stronger vertical motions in the RCM lead to a general increase in dynamical precipitation relative to the GCM, and thus a drier and warmer troposphere and reduced convective cloud and precipitation. Layer-cloud cover is also reduced in the RCM, due to a time-step dependence in the treatment of the dissipation of ice cloud. Significant changes occur in the surface heat balance. The spatial patterns of surface air temperature and precipitation over Europe are well simulated by both the GCM and the RCM on scales resolved by the former. At finer scales the RCM contains a strong signal which is related to orographic height. Validation against a detailed observed climatology for Great Britain demonstrates that this signal contains considerable skill.

Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. precipitation set a record (Fig. 3a). Sustained high precipitation amounts 60 during the whole winter led to this record, rather than a few very wet days, Human influence on climate in the 2014 Southern 61and none of the 5-day precipitation averages over the three winter months 62 was a record (Fig. 3b). Similarly, while Thames' daily peak river flows were 63 not exceptional, the 30-day peak flow was the second highest since 64 measurements began in 1883 ( Supplementary Fig. 10 to provide a conservative estimate of uncertainty. 106We consider January precipitation and SLP, with Southern England 107Precipitation (SEP) averaged over land grid points in 50º-52ºN, 6.5ºW-2ºE. 189In the large RCM ensemble, the best estimate for the overall change in risk of is an increase of 43%, with a range from no change to 164% increase 192 associated with uncertainty in the pattern of anthropogenic warming (Fig. 5d). rainfall that we simulate is less on timescales that dominate flooding in this 252 catchment, consistent with the mechanism being an increase in the frequency 253 of the zonal regime, and so, successions of strong but fast-moving storms. 254Outputs from CLASSIC are combined with information about the location of