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Climate change is shaping extreme heat and rain. To what degree human activity has increased the risk of high impact events is of high public concern and still heavily debated. Recent studies attributed single extreme events to climate change by comparing climate model experiments where the influence of an external driver can be included or artificially suppressed. Many of these results however did not properly account for model errors in simulating the probabilities of extreme event occurrences. Here we show, exploiting advanced correction techniques from the weather forecasting field, that correcting properly for model probabilities alters the attributable risk of extreme events to climate change. This study illustrates the need to correct for this type of model error in order to provide trustworthy assessments of climate change impacts. ; This work has been supported by the EUropean CLimate and weather Events: Interpretation and Attribution (EUCLEIA) project, funded by the European Union's Seventh Framework Programme [FP7/2007–2013] under Grant agreement no. 607085 as well as the Horizon 2020 EUCP EUropean Climate Prediction system under Grant agreement no. 776613 and by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 grant reference RYC-2017-22964. We thank Antje Weisheimer and Tim Palmer from the University of Oxford for their support to conduct this research. We are further indepted to Fraser Lott, Jonas Bhend, Stefan Siegert, Karsten Haustein, and Myles Allen for their help on the analysis and interpretation. O. Bellprat is the corresponding author (omar.bellprat@env.ethz.ch) and carried out the core analysis and writing of the paper. V. Guemas, F. Doblas-Reyes, and M. Donat have all either contributed in the study design or the interpretation of the analysis as well as the writing of the paper ; Peer Reviewed ; Postprint (published version)

Climate change is shaping extreme heat and rain. To what degree human activity has increased the risk of high impact events is of high public concern and still heavily debated. Recent studies attributed single extreme events to climate change by comparing climate model experiments where the influence of an external driver can be included or artificially suppressed. Many of these results however did not properly account for model errors in simulating the probabilities of extreme event occurrences. Here we show, exploiting advanced correction techniques from the weather forecasting field, that correcting properly for model probabilities alters the attributable risk of extreme events to climate change. This study illustrates the need to correct for this type of model error in order to provide trustworthy assessments of climate change impacts. ; This work has been supported by the EUropean CLimate and weather Events: Interpretation and Attribution (EUCLEIA) project, funded by the European Union's Seventh Framework Programme [FP7/2007–2013] under Grant agreement no. 607085 as well as the Horizon 2020 EUCP EUropean Climate Prediction system under Grant agreement no. 776613 and by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 grant reference RYC-2017-22964. We thank Antje Weisheimer and Tim Palmer from the University of Oxford for their support to conduct this research. We are further indepted to Fraser Lott, Jonas Bhend, Stefan Siegert, Karsten Haustein, and Myles Allen for their help on the analysis and interpretation. O. Bellprat is the corresponding author (omar.bellprat@env.ethz.ch) and carried out the core analysis and writing of the paper. V. Guemas, F. Doblas-Reyes, and M. Donat have all either contributed in the study design or the interpretation of the analysis as well as the writing of the paper ; Peer Reviewed ; Postprint (published version)

Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements are more difficult to detect at middle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction. ; The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under Grant Agreements 308378 (SPECS), 603521 (PREFACE), and 607085 (EUCLEIA), the Horizon 2020 EU program under Grant Agreements 641727 (PRIMAVERA) and 641811 (IMPREX), and the ESA Climate Change Initiative (CCI) Living Planet Fellowship VERITAS-CCI. We acknowledge PRACE for awarding access to Marenostrum3 based in Spain at the Barcelona Supercomputing Center through the HiResClim project. We acknowledge the work of the developers of the s2dverification R-based package (http://cran.r-project. org/web/packages/s2dverification/index.html) and autosubmit workflow manager (https://pypi.python.org/ pypi/autosubmit/3.5.0). Paolo Davini acknowledges the funding from the European Union's Horizon 2020 research and innovation programme COGNAC under the European Union Marie Sklodowska-Curie Grant Agreement 654942. ; Peer Reviewed ; Postprint (published version)

Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements are more difficult to detect at middle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction. ; The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under Grant Agreements 308378 (SPECS), 603521 (PREFACE), and 607085 (EUCLEIA), the Horizon 2020 EU program under Grant Agreements 641727 (PRIMAVERA) and 641811 (IMPREX), and the ESA Climate Change Initiative (CCI) Living Planet Fellowship VERITAS-CCI. We acknowledge PRACE for awarding access to Marenostrum3 based in Spain at the Barcelona Supercomputing Center through the HiResClim project. We acknowledge the work of the developers of the s2dverification R-based package (http://cran.r-project. org/web/packages/s2dverification/index.html) and autosubmit workflow manager (https://pypi.python.org/ pypi/autosubmit/3.5.0). Paolo Davini acknowledges the funding from the European Union's Horizon 2020 research and innovation programme COGNAC under the European Union Marie Sklodowska-Curie Grant Agreement 654942. ; Peer Reviewed ; Postprint (published version)

Under the influence of global warming, heatwaves are becoming a major threat in many parts of the world, affecting human health and mortality, food security, forest fires, biodiversity, energy consumption, as well as the production and transportation networks. Seasonal forecasting is a promising tool to help mitigate these impacts on society. Previous studies have highlighted some predictive capacity of seasonal forecast systems for specific strong heatwaves such as those of 2003 and 2010. To our knowledge, this study is thus the first of its kind to systematically assess the prediction skill of heatwaves over Europe in a state-of-the-art seasonal forecast system. One major prerequisite to do so is to appropriately define heatwaves. Existing heatwave indices, built to measure heatwave duration and severity, are often designed for specific impacts and thus have limited robustness for an analysis of heatwave variability. In this study, we investigate the seasonal prediction skill of European summer heatwaves in the ECMWF System 5 operational forecast system by means of several dedicated metrics, as well as its added-value compared to a simple statistical model based on the linear trend. We are able to show, for the first time, that seasonal forecasts initialized in early May can provide potentially useful information of summer heatwave propensity, which is the tendency of a season to be predisposed to the occurrence of heatwaves. ; This work is a contribution to the HyMeX program through the project ERA4CS-MEDSCOPE, co-funded by the Horizon 2020 Framework Program of the European Union (Grant agreement 690462). Chloe Prodhomme and Javier García-Serrano were supported by the Spanish Juan de la Cierva (IJCI-2016-30802) and Ramon y Cajal (RYC-2016-21181) programs, respectively. Rachel H. White received funding from the European Unions Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement 797961. ; Peer Reviewed ; Postprint (published version)

A set of ensemble integrations from the Coupled Model Intercomparison Project phase 5, with historical forcing plus RCP4.5 scenario, are used to explore if state-of-the-art climate models are able to simulate previously reported linkages between sea-ice concentration (SIC) anomalies over the eastern Arctic, namely in the Greenland–Barents–Kara Seas, and lagged atmospheric circulation that projects on the North Atlantic Oscillation (NAO)/Arctic Oscillation (AO). The study is focused on variability around the long-term trends, so that all anomalies are detrended prior to analysis; the period of study is 1979–2013. The model linkages are detected by applying maximum covariance analysis. As also found in observational data, all the models considered here show a statistically significant link with sea-ice reduction over the eastern Arctic followed by a negative NAO/AO-like pattern. If the simulated relationship is found at a lag of one month, the results suggest that a stratospheric pathway could be at play as the driving mechanism; in observations this is preferentially shown for SIC in November. The interference of a wave-like anomaly over Eurasia, accompanying SIC changes, with the climatological wave pattern appears to be key in setting the mediating role of the stratosphere. On the other hand, if the simulated relationship is found at a lag of two months, the results suggest that tropospheric dynamics are dominant, presumably due to transient eddy feedback; in observations this is preferentially shown for SIC in December. The results shown here and previous evidence from atmosphere-only experiments emphasize that there could be a detectable influence of eastern Arctic SIC variability on mid-latitude atmospheric circulation anomalies. Even if the mechanisms are robust among the models, the timing of the simulated linkages strongly depends on the model and does not generally mimic the observational ones. This implies that the atmospheric sensitivity to sea-ice changes largely depends on the mean-flow and parameterizations, which could lead to misleading conclusions elsewhere if a multi-model ensemble-mean approach is adopted. It might also represent an important source of uncertainty in climate prediction and projection. Modelling efforts are hence further required to improve representation of the background atmospheric circulation and reduce biases, in order to attain more accurate covariability. ; The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement No. 308299 (NACLIM—www.naclim.eu). JG-S was partially supported by the H2020-funded MSCA-IF-EF DPETNA project (GA No. 655339). This work was also supported by the BMBF project CLIMPRE Inter-Dec (FKZ:01LP1609A; DM) and the RCN project KLIMAFORSK InterDec (260393; YG) within the framework of JPI CLIM Belmont-Forum InterDec consortial project. JG-S and MPK thank the 6th Severo Ochoa mobility programme for funding the visit of the latter to BSC-CNS in October 2015. The authors thank Ileana Bladé (UB, Spain) and Lantao Sun (CIRES-NOAA/ESRL, USA) for useful discussions, and Neven S. Fuckar (BSC-CNS) for his help during the review process. Technical support at BSC-CNS (Computational Earth Sciences group) is sincerely acknowledged. The authors are also grateful to the anonymous reviewers for their comments, which helped to improve the clarity of the manuscript. ; Peer Reviewed ; Postprint (author's final draft)

A set of ensemble integrations from the Coupled Model Intercomparison Project phase 5, with historical forcing plus RCP4.5 scenario, are used to explore if state-of-the-art climate models are able to simulate previously reported linkages between sea-ice concentration (SIC) anomalies over the eastern Arctic, namely in the Greenland–Barents–Kara Seas, and lagged atmospheric circulation that projects on the North Atlantic Oscillation (NAO)/Arctic Oscillation (AO). The study is focused on variability around the long-term trends, so that all anomalies are detrended prior to analysis; the period of study is 1979–2013. The model linkages are detected by applying maximum covariance analysis. As also found in observational data, all the models considered here show a statistically significant link with sea-ice reduction over the eastern Arctic followed by a negative NAO/AO-like pattern. If the simulated relationship is found at a lag of one month, the results suggest that a stratospheric pathway could be at play as the driving mechanism; in observations this is preferentially shown for SIC in November. The interference of a wave-like anomaly over Eurasia, accompanying SIC changes, with the climatological wave pattern appears to be key in setting the mediating role of the stratosphere. On the other hand, if the simulated relationship is found at a lag of two months, the results suggest that tropospheric dynamics are dominant, presumably due to transient eddy feedback; in observations this is preferentially shown for SIC in December. The results shown here and previous evidence from atmosphere-only experiments emphasize that there could be a detectable influence of eastern Arctic SIC variability on mid-latitude atmospheric circulation anomalies. Even if the mechanisms are robust among the models, the timing of the simulated linkages strongly depends on the model and does not generally mimic the observational ones. This implies that the atmospheric sensitivity to sea-ice changes largely depends on the mean-flow and parameterizations, which could lead to misleading conclusions elsewhere if a multi-model ensemble-mean approach is adopted. It might also represent an important source of uncertainty in climate prediction and projection. Modelling efforts are hence further required to improve representation of the background atmospheric circulation and reduce biases, in order to attain more accurate covariability. ; The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement No. 308299 (NACLIM—www.naclim.eu). JG-S was partially supported by the H2020-funded MSCA-IF-EF DPETNA project (GA No. 655339). This work was also supported by the BMBF project CLIMPRE Inter-Dec (FKZ:01LP1609A; DM) and the RCN project KLIMAFORSK InterDec (260393; YG) within the framework of JPI CLIM Belmont-Forum InterDec consortial project. JG-S and MPK thank the 6th Severo Ochoa mobility programme for funding the visit of the latter to BSC-CNS in October 2015. The authors thank Ileana Bladé (UB, Spain) and Lantao Sun (CIRES-NOAA/ESRL, USA) for useful discussions, and Neven S. Fuckar (BSC-CNS) for his help during the review process. Technical support at BSC-CNS (Computational Earth Sciences group) is sincerely acknowledged. The authors are also grateful to the anonymous reviewers for their comments, which helped to improve the clarity of the manuscript. ; Peer Reviewed ; Postprint (author's final draft)

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992–2010 period performed by five different global coupled ocean–atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land–atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions. ; The authors thank Jeff Knight (Met Office Hadley Centre) for his constructive comments on earlier versions of this manuscript. The research leading to these results received funding from the European Union Seventh Framework Programme (FP7/2007–2013) SPECS project (Grant Agreement Number 308378) and H2020 Framework Programme IMPREX project (Grant Agreement Number 641811). Constantin Ardilouze was also supported by the BSC Centro de Excelencia Severo Ochoa Programme. ; Peer Reviewed ; Postprint (author's final draft)

Dynamical forecast systems have low to moderate skill in continental winter predictions in the extratropics. Here we assess the multimodel predictive skill over Northern Hemisphere high latitudes and midlatitudes using four state‐of‐the‐art forecast systems. Our main goal was to quantify the impact of the Arctic sea ice state during November on the sea level pressure (SLP), surface temperature, and precipitation skill during the following winter. Interannual variability of the November Barents and Kara Sea ice is associated with an important fraction of December to February (DJF) prediction skill in regions of Eurasia. We further show that skill related to sea ice in these regions is accompanied with enhanced skill of DJF SLP in western Russia, established by a sea ice‐atmosphere teleconnection mechanism. The teleconnection is strongest when atmospheric blocking conditions in Scandinavia/western Russia in November reduce a systematic SLP bias that is present in all systems. ; This work was funded by the European Union projects APPLICATE (Grant 727862), PRIMAVERA (Grant 641727), INTAROS (Grant 727890), and ESA/CMUG‐CCI3. We acknowledge PRACE for awarding us access to MareNostrum IV at Barcelona Supercomputing Center (BSC), Spain. J. C. A. N. received financial support from the Spanish Ministerio de Ciencia,Innovación y Universidades through a Juan de la Cierva personal grant (FJCI‐2017‐34027). E. T. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant Agreement 748750 (SPFireSD project). V. G. received funding from the Agence Nationale de la Recherche through the Make Our Planet Great Again Grant ANR‐17‐MPGA‐003. The data from EC‐Earth3.2 and CNRM‐CM6‐1 are publicly available (at https://applicate.eu/data/data‐portal). GloSea5 (v13) and SEAS5 data are publicly available (at https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal‐ monthly‐single‐levels?tab=overview). All the data were downloaded from their original source, converted to NetCDF in a format designed for efficient analysis with the tools mentioned before, and quality checked at several levels. ; Peer Reviewed ; Postprint (published version)

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992–2010 period performed by five different global coupled ocean–atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land–atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions. ; The authors thank Jeff Knight (Met Office Hadley Centre) for his constructive comments on earlier versions of this manuscript. The research leading to these results received funding from the European Union Seventh Framework Programme (FP7/2007–2013) SPECS project (Grant Agreement Number 308378) and H2020 Framework Programme IMPREX project (Grant Agreement Number 641811). Constantin Ardilouze was also supported by the BSC Centro de Excelencia Severo Ochoa Programme. ; Peer Reviewed ; Postprint (author's final draft)