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Future precipitation changes are typically estimated from climate model simulations, while the credibility of such projections needs to be assessed by their ability to capture observed precipitation changes. Here we evaluate how skillfully historical climate simulations contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) capture observed changes in mean and extreme precipitation. We find that CMIP6 historical simulations skillfully represent observed precipitation changes over large parts of Europe, Asia, northeastern North America, parts of South America and western Australia, whereas a lack of skill is apparent in western North America and parts of Africa. In particular in regions with moderate skill the availability of very large ensembles can be beneficial to improve the simulation accuracy. CMIP6 simulations are regionally skillful where they capture observed (positive or negative) trends, whereas a lack of skill is found in regions characterized by negative observed precipitation trends where CMIP6 simulates increases. Plain Language Summary Climate models are the primary tools to predict future changes in precipitation related to global warming. These predictions can however only usefully inform adaptation measures if they can be trusted. Here we evaluate the trustworthiness of climate model‐simulated precipitation changes based on their capability to correctly capture observed precipitation changes. We apply skill measures commonly used for the evaluation of seasonal to decadal climate predictions to historical climate simulations. We perform this analysis for total precipitation accumulations and indicators of precipitation extremes. The level of skill differs between regions and can be sensitive to the number of available simulations, with some regions benefitting from very large simulation ensembles. Mean and extreme precipitation are skillfully predicted in similar regions, including large parts of Europe and Asia. Lack of skill typically occurs in regions where observed precipitation is characterized by downward trends but Coupled Model Intercomparison Project Phase 6 models simulate increases. This study helps understand the trustworthiness of climate simulations to realistically capture precipitation changes, identifying regions where current models are more or less capable. Key Points Coupled Model Intercomparison Project Phase 6 (CMIP6) realistically simulates observed changes in mean and extreme precipitation in large parts of Europe and Asia and other land regions In regions with moderate skill and observed precipitation subject to multi‐decadal variations the availability of very large ensembles is beneficial Lack of skill occurs primarily in regions where negative precipitation trends are observed but CMIP6 simulates increases

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts-System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction.

Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980-2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.

One of the most robust remote impacts of El Niño–Southern Oscillation (ENSO) is the teleconnection to tropical North Atlantic (TNA) sea surface temperature (SST) in boreal spring. However, important questions still remain open. In particular, the timing of the ENSO–TNA relationship lacks understanding. The three previously proposed mechanisms rely on teleconnection dynamics involving a time lag of one season with respect to the ENSO mature phase in winter, but recent results have shown that the persistence of ENSO into spring is necessary for the development of the TNA SST anomalies. Likewise, the identification of the effective atmospheric forcing in the deep TNA to drive the regional air–sea interaction is also lacking. In this manuscript a new dynamical framework to understand the ENSO–TNA teleconnection is proposed, in which a continuous atmospheric forcing is present throughout the ENSO decaying phase. Observational datasets in the satellite era, which include reliable estimates over the ocean, are used to illustrate the mechanism at play. The dynamics rely on the remote Gill-type response to the ENSO zonally compensated heat source over the Amazon basin, associated with perturbations in the Walker circulation. For El Niño conditions, the anomalous diabatic heating in the tropical Pacific is compensated by anomalous diabatic cooling, in association with negative rainfall anomalies and descending motion over northern South America. A pair of anomalous cyclonic circulations is established at upper-tropospheric levels in the tropical Atlantic straddling the equator, displaying a characteristic baroclinic structure with height. In the TNA region, the mirrored anomalous anticyclonic circulation at lower-tropospheric levels weakens the northeasterly trade winds, leading to a reduction in evaporation and of the ocean mixed layer depth, hence to positive SST anomalies. Apart from the dominance of latent heat flux anomalies in the remote response, sensible heat flux and shortwave radiation anomalies also appear to contribute. The “lagged” relationship between mature ENSO in winter and peaking TNA SSTs in spring seems to be phase locked with the seasonal cycle in both the location of the mechanism’s centers of action and regional SST variance.

The European summer 2003 is a prominent example for an extreme hot and dry season. The main mechanisms that contributed to the growth of the heat wave are still disputed and state‐of‐the‐art climate models have difficulty to realistically simulate the extreme conditions. Here we analyse simulations using recent versions of the European Centre for Medium‐Range Weather Forecasts seasonal ensemble forecasting system and present, for the first time, retrospective forecasts which simulate accurately not only the abnormal warmth but also the observed precipitation and mid‐tropospheric circulation patterns. It is found that while the land surface hydrology plays a crucial role, the successful simulations also required revised formulations of the radiative and convective parameterizations. We conclude that the predictability of the event was less due to remote teleconnections effects and more due to in situ processes which helped maintain the dry surface anomalies occurring at the beginning of the summer.

Clouds play a key role in the climate of the Arctic region. Observational evidence suggests that sea‐ice loss fosters increased cloud cover due to enhanced surface turbulent fluxes. Yet, it is not clear whether this mechanism is (well) represented in climate models. In this study we analyze the simulated response of low clouds to sea‐ice loss in a set of dedicated numerical model experiments prescribed with changes in sea ice only. We find large discrepancies between models regarding their representation of low cloud responses to identical sea‐ice loss. We propose a physical explanation that links biases in simulated present‐day surface temperature and stratification to the sign of the low cloud response to sea‐ice loss. Our results suggest that mean‐state temperature biases need to be reduced in order to narrow uncertainty in the simulated cloud response to sea‐ice loss.

Societal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate-fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions.

Facing increasing risks from climate change, governments at all levels have started to mainstream the use of climate information. It has been widely acknowledged that the inclusion of stakeholder knowledge and needs, for example, in a co‐design and co‐production process, is important for producing user‐relevant information. Here we start from a hypothetical example and two real‐world case studies from South America and West Africa to discuss the role of user values, power relationships and language in the construction of climate information. While these aspects have been discussed individually in several papers, we focus on the mutual influences of these aspects in the information construction and argue that, therefore, they cannot be considered separately. We identify five dimensions—the level of risk, the complexity of the scientific problem, user values, power relationships and language—to characterize the complexity of a given user context. Analyzing these dimensions can guide the choice and design of user engagement in a given situation. In particular, even basic research may benefit from such an engagement. Regularly accounting for these aspects in research projects may require substantial changes in the way research funding is organized and how the work of researchers is rewarded. Plain Language Summary Facing increasing risks from climate change, local, national and transnational governments have started to support and require the use of climate information in private and public investments. It is widely known that including the knowledge and needs of information users is important for producing user‐relevant information. Here we discuss, for this process, the role of what is important for users, how influential some users are, and which background information and technical terms users are familiar with. We illustrate our arguments with a made‐up example and two real‐world case studies from South America and West Africa. While these aspects have been discussed separately before, we focus on how they influence each other. We argue that they cannot be considered separately. We explain how understanding these aspects in the specific situation of users, for example, engineers, risk analysts or decision makers, can help setting up a useful collaboration with the users. To account for these aspects in regular research, funding agencies would have to provide the necessary funding for a close collaboration with users. Key Points Accounting for stakeholder values, power relationships and language is important to construct user‐relevant climate information These aspects cannot be treated separately as they influence each other in the information construction Risk level, scientific complexity, user values, power and language guide the choice and design of user engagement in a given situation

In recent years the global warming trend has plateaued, despite increasing anthropogenic emissions. Now research attributes this plateau to an increase in ocean heat uptake, through retrospective predictions of up to 5 years in length. The ability to hindcast this warming plateau strengthens our confidence in the robustness of climate models. Despite a sustained production of anthropogenic greenhouse gases, the Earth’s mean near-surface temperature paused its rise during the 2000–2010 period 1 . To explain such a pause, an increase in ocean heat uptake below the superficial ocean layer 2 , 3 has been proposed to overcompensate for the Earth’s heat storage. Contributions have also been suggested from the deep prolonged solar minimum 4 , the stratospheric water vapour 5 , the stratospheric 6 and tropospheric aerosols 7 . However, a robust attribution of this warming slowdown has not been achievable up to now. Here we show successful retrospective predictions of this warming slowdown up to 5 years ahead, the analysis of which allows us to attribute the onset of this slowdown to an increase in ocean heat uptake. Sensitivity experiments accounting only for the external radiative forcings do not reproduce the slowdown. The top-of-atmosphere net energy input remained in the [0.5–1] W m −2 interval during the past decade, which is successfully captured by our predictions. Most of this excess energy was absorbed in the top 700 m of the ocean at the onset of the warming pause, 65% of it in the tropical Pacific and Atlantic oceans. Our results hence point at the key role of the ocean heat uptake in the recent warming slowdown. The ability to predict retrospectively this slowdown not only strengthens our confidence in the robustness of our climate models, but also enhances the socio-economic relevance of operational decadal climate predictions.

Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.