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Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements—especially reductions in uncertainty—in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. Our findings have implications for managing adaptation to a changing climate. Because the costs of adaptation are very large, and greater uncertainty about future climate is likely to be associated with more expensive adaptation, reducing uncertainty in climate predictions is potentially of enormous economic value. We highlight the need for much more work to compare (a) the cost of various degrees of adaptation, given current levels of uncertainty and (b) the cost of new investments in climate science to reduce current levels of uncertainty. Our study also highlights the importance of targeting climate science investments on the most promising opportunities to reduce prediction uncertainty.

A Jedi-wannabe and sports nut, Ed Hawkins pondered: what if a coach or athlete had tried to harness such mysterious powers? They would be unstoppable. This set Ed off on an extraordinary adventure across the West Coast of America in search of a superhuman sports star. He discovers cosmic thinkers who, back in their 1960s heyday, believed that through the power of thought alone a superhuman could be created. One that could see into the future, slow down time and control minds. So successful were their tactics that they attracted the attention of the US government. Meanwhile in Russia their Soviet counterparts were employing equally bewildering brain power. Their goal? To win the Cold War. And so from the 1970s and into the Eighties the underground free-thinking movement became a fully-funded state secret in an `inner space race' between the US and the Soviet Union. Both sides attempting to create the perfect human killing machine. It worked. Sort of. Instead of building a super soldier, the mystics from both sides came together to preach peace and love to their political paymasters. After the thaw, the search for the superhuman sports star began again and continues to this day. In The Men on Magic Carpets Ed goes deep into a secret network of supernatural sages and is told about a mysterious American football coach who made it to the top by teaching his players The Force. But can he be found? Will he admit to what he truly believes? And how does our intrepid author cope with his own brush with the Dark Side as the shadowy military once again attempt to use the mystical powers for ill.

We separate and quantify the sources of uncertainty in projections of regional (∼2,500 km) precipitation changes for the twenty-first century using the CMIP3 multi-model ensemble, allowing a direct comparison with a similar analysis for regional temperature changes. For decadal means of seasonal mean precipitation, internal variability is the dominant uncertainty for predictions of the first decade everywhere, and for many regions until the third decade ahead. Model uncertainty is generally the dominant source of uncertainty for longer lead times. Scenario uncertainty is found to be small or negligible for all regions and lead times, apart from close to the poles at the end of the century. For the global mean, model uncertainty dominates at all lead times. The signal-to-noise ratio (S/N) of the precipitation projections is highest at the poles but less than 1 almost everywhere else, and is far lower than for temperature projections. In particular, the tropics have the highest S/N for temperature, but the lowest for precipitation. We also estimate a ‘potential S/N’ by assuming that model uncertainty could be reduced to zero, and show that, for regional precipitation, the gains in S/N are fairly modest, especially for predictions of the next few decades. This finding suggests that adaptation decisions will need to be made in the context of high uncertainty concerning regional changes in precipitation. The potential to narrow uncertainty in regional temperature projections is far greater. These conclusions on S/N are for the current generation of models; the real signal may be larger or smaller than the CMIP3 multi-model mean. Also note that the S/N for extreme precipitation, which is more relevant for many climate impacts, may be larger than for the seasonal mean precipitation considered here.

The United Nations Framework Convention on Climate Change (UNFCCC) process agreed in Paris to limit global surface temperature rise to “well below 2°C above pre-industrial levels.” But what period is preindustrial? Somewhat remarkably, this is not defined within the UNFCCC’s many agreements and protocols. Nor is it defined in the IPCC’s Fifth Assessment Report (AR5) in the evaluation of when particular temperature levels might be reached because no robust definition of the period exists. Here we discuss the important factors to consider when defining a preindustrial period, based on estimates of historical radiative forcings and the availability of climate observations. There is no perfect period, but we suggest that 1720–1800 is the most suitable choice when discussing global temperature limits. We then estimate the change in global average temperature since preindustrial using a range of approaches based on observations, radiative forcings, global climate model simulations, and proxy evidence. Our assessment is that this preindustrial period was likely 0.55°–0.80°C cooler than 1986–2005 and that 2015 was likely the first year in which global average temperature was more than 1°C above preindustrial levels. We provide some recommendations for how this assessment might be improved in the future and suggest that reframing temperature limits with a modern baseline would be inherently less uncertain and more policy relevant.

The ongoing emission of greenhouse gases (GHGs) is triggering changes in many climate hazards that can impact humanity. We found traceable evidence for 467 pathways by which human health, water, food, economy, infrastructure and security have been recently impacted by climate hazards such as warming, heatwaves, precipitation, drought, floods, fires, storms, sea-level rise and changes in natural land cover and ocean chemistry. By 2100, the world’s population will be exposed concurrently to the equivalent of the largest magnitude in one of these hazards if emmisions are aggressively reduced, or three if they are not, with some tropical coastal areas facing up to six simultaneous hazards. These findings highlight the fact that GHG emissions pose a broad threat to humanity by intensifying multiple hazards to which humanity is vulnerable.

The study of Decadal Climate Variability (DCV) and Predictability is the interdisciplinary endeavor to characterize, understand, attribute, simulate, and predict the slow, multiyear variations of climate at global (e.g., the recent slowdown of global mean temperature rise in the early 2000s) and regional (e.g., decadal modulation of hurricane activity in the Atlantic, ongoing drought in California or in the Sahel in the 1970s–80s, etc.) scales. This study remains very challenging despite decades of research, extensive progress in climate system modeling, and improvements in the availability and coverage of a wide variety of observations. Considerable obstacles in applying this knowledge to actual predictions remain. This short article is a succint review paper about DCV and predictability. Based on listed issues and priorities, it also proposes a unifying theme referred to as “drivers of teleconnectivity” as a backbone to address and structure the core DCV research challenge. This framework goes beyond a preoccupation with changes in the global mean temperature and directly addresses the regional impacts of external (natural and anthropogenic) climate forcing and internal climate interactions; it thus explicitly deals with the societal needs for region-specific climate information. Such a framework also enables the integration of efforts in a large international research community toward advancing the observation, characterization, understanding, and prediction of DCV. Recommendations to make progress are provided as part of the contribution of the CLIVAR “DCVP Research Focus” group.

As global surface temperatures continue to rise, both the duration and the intensity of heat waves across most land areas are expected to increase. The 2022 European summer broke a number of temperature records where a new record daily maximum temperature of 40.3°C was reached on 19th July making it the hottest July heat wave event in the UK. This paper aims to detect and analyse historical heat wave events, particularly prior to 1927 and compare these with recent events, particularly, 2022, which featured four summer heat wave events in the UK. This allows us to understand how noteworthy historical extremes are in comparison to those in recent decades, to place modern events into historical context, and to extend the sample of extreme events. Summer heat wave events have been detected between 1878 and 2022 from long station data in the UK. Heat wave extent, duration, and intensity have been analysed to compare past heat waves to the recent 2022 heat waves. For each of the summer months at least one of the top 10 most intense events between 1878 and 2022 occurred in the earliest third of the dataset (before 1927) emphasising the value of analysing early heat events. In all detected events, the anomalous UK heat was part of large‐scale European extreme heat when examining 20th‐century reanalysis data, associated with a high‐pressure system. The 2022 July event resembles in pattern of warming and circulation some earlier events, for example, in 1925. While there is a clear trend in the monthly data and the overall frequency of anomalously hot days, heat wave activity on daily scales even in the period 1878 and 1926 is considerable and in some cases comparable to modern heat wave events in the UK. The most intense events detected led to societal impacts based on UK newspaper articles from the period including impacts on the agricultural sector, health impacts, and travel disruptions, broadly comparable to impacts from recent events. In this article, we analyse how historically observed extreme heat events can provide useful case studies for extreme heat and make comparisons to recent heat wave events, particularly in 2022. While there is a clear warming trend in the monthly data, heat wave activity on daily scales between 1878 and 1926 is considerable and comparable in some cases to modern heat wave events in the UK, however, the July 2022 heat wave was the most intense to have occurred on record. Detected events show large‐scale European extreme heat in 20th‐century reanalysis data, associated with a high‐pressure system over parts of Europe. Monthly temperature data capture these detected events reasonably well, with above‐average monthly temperatures aligning with months in which at least one heat wave event occurred. The most intense events detected lead to noticeable societal impacts based on newspaper articles from the period.

This review addresses the causes of observed climate variations across the industrial period, from 1750 to present. It focuses on long-term changes, both in response to external forcing and to climate variability in the ocean and atmosphere. A synthesis of results from attribution studies based on palaeoclimatic reconstructions covering the recent few centuries to the 20th century, and instrumental data shows how greenhouse gases began to cause warming since the beginning of industrialization, causing trends that are attributable to greenhouse gases by 1900 in proxy-based temperature reconstructions. Their influence increased over time, dominating recent trends. However, other forcings have caused substantial deviations from this emerging greenhouse warming trend: volcanic eruptions have caused strong cooling following a period of unusually heavy activity, such as in the early 19th century; or warming during periods of low activity, such as in the early-to-mid 20th century. Anthropogenic aerosol forcing most likely masked some global greenhouse warming over the 20th century, especially since the accelerated increase in sulphate aerosol emissions starting around 1950. Based on modelling and attribution studies, aerosol forcing has also influenced regional temperatures, caused long-term changes in monsoons and imprinted on Atlantic variability. Multi-decadal variations in atmospheric modes can also cause long-term climate variability, as apparent for the example of the North Atlantic Oscillation, and have influenced Atlantic ocean variability. Long-term precipitation changes are more difficult to attribute to external forcing due to spatial sparseness of data and noisiness of precipitation changes, but the observed pattern of precipitation response to warming from station data supports climate model simulated changes and with it, predictions. The long-term warming has also led to significant differences in daily variability as, for example, visible in long European station data. Extreme events over the historical record provide valuable samples of possible extreme events and their mechanisms.

This Review considers recent advances in our understanding of regional climate change, critically discusses outstanding issues, and recommends targets for future research. Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.