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

In the summer 2010 Western Russia was hit by an extraordinary heat wave, with the region experiencing by far the warmest July since records began. Whether and to what extent this event is attributable to anthropogenic climate change is controversial. Dole et al. (2011) report the 2010 Russian heat wave was “mainly natural in origin” whereas Rahmstorf and Coumou (2011) write that with a probability of 80% “the 2010 July heat record would not have occurred” without the large‐scale climate warming since 1980, most of which has been attributed to the anthropogenic increase in greenhouse gas concentrations. The latter explicitly state that their results “contradict those of Dole et al. (2011).” Here we use the results from a large ensemble simulation experiment with an atmospheric general circulation model to show that there is no substantive contradiction between these two papers, in that the same event can be both mostly internally‐generated in terms of magnitude and mostly externally‐driven in terms of occurrence‐probability. The difference in conclusion between these two papers illustrates the importance of specifying precisely what question is being asked in addressing the issue of attribution of individual weather events to external drivers of climate.

Abstract. The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 • C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 • C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climatePublished by Copernicus Publications on behalf of the European Geosciences Union. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), the second two being estimates of a similar decade but under 1.5 and 2 • C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 • C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 • C scenario.

Demonstrating the effect that climate change is having on regional weather is a subject which occupies climate scientists, government policy makers and the media. After an extreme weather event occurs, the question is often posed, 'Was the event caused by anthropogenic climate change?' Recently, a new branch of climate science (known as attribution) has sought to quantify how much the risk of extreme events occurring has increased or decreased due to climate change. One method of attribution uses very large ensembles of climate models computed via volunteer distributed computing. A recent advancement is the ability to run both a global climate model and a higher resolution regional climate model on a volunteer's home computer. Such a set-up allows the simulation of weather on a scale that is of most use to studies of the attribution of extreme events. This article introduces a global climate model that has been developed to simulate the climatology of all major land regions with reasonable accuracy. This then provides the boundary conditions to a regional climate model (which uses the same formulation but at higher resolution) to ensure that it can produce realistic climate and weather over any region of choice. The development process is documented and a comparison to previous coupled climate models and atmosphere-only climate models is made. The system (known as weather@home) by which the global model is coupled to a regional climate model and run on volunteers' home computers is then detailed. Finally, a validation of the whole system is performed, with a particular emphasis on how accurately the distributions of daily mean temperature and daily mean precipitation are modelled in a particular application over Europe. This builds confidence in the applicability of the weather@home system for event attribution studies.

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