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It has been predicted, by theory and models, that heavy precipitation will increase with climate change and this is now being seen in observations. Emergence of signals such as this will enable testing of predictions, which should increase confidence in them. Environmental phenomena are often observed first, and then explained quantitatively. The complexity of processes, the range of scales involved, and the lack of first principles make it challenging to predict conditions beyond the ones observed. Here we use the intensification of heavy precipitation as a counterexample, where seemingly complex and potentially computationally intractable processes manifest themselves to first order in simple ways: heavy precipitation intensification is now emerging in the observed record across many regions of the world, confirming both theory and model predictions made decades ago. As the anthropogenic climate signal strengthens, there will be more opportunities to test climate predictions for other variables against observations and across a hierarchy of different models and theoretical concepts.

The contribution of human-induced climate change to global heavy precipitation and hot extreme events is quantified. The results show that of the moderate extremes, 18% of precipitation and 75% of high-temperature events are attributable to warming. Climate change includes not only changes in mean climate but also in weather extremes. For a few prominent heatwaves and heavy precipitation events a human contribution to their occurrence has been demonstrated 1 , 2 , 3 , 4 , 5 . Here we apply a similar framework but estimate what fraction of all globally occurring heavy precipitation and hot extremes is attributable to warming. We show that at the present-day warming of 0.85 °C about 18% of the moderate daily precipitation extremes over land are attributable to the observed temperature increase since pre-industrial times, which in turn primarily results from human influence 6 . For 2 °C of warming the fraction of precipitation extremes attributable to human influence rises to about 40%. Likewise, today about 75% of the moderate daily hot extremes over land are attributable to warming. It is the most rare and extreme events for which the largest fraction is anthropogenic, and that contribution increases nonlinearly with further warming. The approach introduced here is robust owing to its global perspective, less sensitive to model biases than alternative methods and informative for mitigation policy, and thereby complementary to single-event attribution. Combined with information on vulnerability and exposure, it serves as a scientific basis for assessment of global risk from extreme weather, the discussion of mitigation targets, and liability considerations.

Recent climate extremes have broken long-standing records by large margins. Such extremes unprecedented in the observational period often have substantial impacts due to a tendency to adapt to the highest intensities, and no higher, experienced during a lifetime. Here, we show models project not only more intense extremes but also events that break previous records by much larger margins. These record-shattering extremes, nearly impossible in the absence of warming, are likely to occur in the coming decades. We demonstrate that their probability of occurrence depends on warming rate, rather than global warming level, and is thus pathway-dependent. In high-emission scenarios, week-long heat extremes that break records by three or more standard deviations are two to seven times more probable in 2021–2050 and three to 21 times more probable in 2051–2080, compared to the last three decades. In 2051–2080, such events are estimated to occur about every 6–37 years somewhere in the northern midlatitudes.Changes in extreme heat are often calculated as anomalies above a reference climatology. A different definition—week-day heatwaves surpassing the current record by large margins—shows that their occurrence probabilities depend on warming rate, not level, and are higher than during recent decades.

There are large uncertainties associated with the projection of climate extremes. This study shows that the uncertainties are mainly due to internal climate variability. However, model projections are consistent when averaged across regions, allowing robust projection of future extremes. Many climatic extremes are changing 1 , 2 , 3 , 4 , 5 , and decision-makers express a strong need for reliable information on further changes over the coming decades as a basis for adaptation strategies. Here, we demonstrate that for extremes stakeholders will have to deal with large irreducible uncertainties on local to regional scales as a result of internal variability, even if climate models improve rapidly. A multimember initial condition ensemble carried out with an Earth system model shows that trends towards more intense hot and less intense cold extremes may be masked or even reversed locally for the coming three to five decades even if greenhouse gas emissions rapidly increase. Likewise, despite a long-term trend towards more intense precipitation and longer dry spells, multidecadal trends of opposite sign cannot be excluded over many land points. However, extremes may dramatically change at a rate much larger than anticipated from the long-term signal. Despite these large irreducible uncertainties on the local scale, projections are remarkably consistent from an aggregated spatial probability perspective. Models agree that within only three decades about half of the land fraction will see significantly more intense hot extremes. We show that even in the short term the land fraction experiencing more intense precipitation events is larger than expected from internal variability. The proposed perspective yields valuable information for decision-makers and stakeholders at the international level.

Owing to the turbulent nature of the ocean, mesoscale eddies are omnipresent. An analysis of atmospheric conditions associated with several hundred thousand eddies in the Southern Ocean suggests that the transitory sea surface temperature fronts associated with these eddies alter near-surface winds, clouds and rainfall. Owing to the turbulent nature of the ocean, mesoscale eddies are omnipresent. The impact of these transitory and approximately circular sea surface temperature fronts on the overlying atmosphere is not well known. Stationary fronts such as the Gulf Stream have been reported to lead to pronounced atmospheric changes 1 , 2 . However, the impact of transient ocean eddies on the atmosphere has not been determined systematically, except on winds and to some extent clouds 3 , 4 , 5 , 6 . Here, we examine the atmospheric conditions associated with over 600,000 individual eddies in the Southern Ocean, using satellite data. We show that ocean eddies locally affect near-surface wind, cloud properties and rainfall. The observed pattern of atmospheric change is consistent with a mechanism in which sea surface temperature anomalies associated with the oceanic eddies modify turbulence in the atmospheric boundary layer. In the case of cyclonic eddies, this modification triggers a slackening of near-surface winds, a decline in cloud fraction and water content, and a reduction in rainfall. We conclude that transient mesoscale ocean structures can significantly affect much larger atmospheric low-pressure systems that swiftly pass by at the latitudes investigated.

Climate change projections are often given as equally weighted averages across ensembles of climate models, despite the fact that the sampling of the underlying ensembles is unclear. We show that a hierarchical clustering of a metric of spatial and temporal variations of either surface temperature or precipitation in control simulations can capture many model relationships across different ensembles. Strong similarities are seen between models developed at the same institution, between models sharing versions of the same atmospheric component, and between successive versions of the same model. A perturbed parameter ensemble of a model appears separate from other structurally different models. The results provide insight into intermodel relationships, into how models evolve through successive generations, and suggest that assuming model independence in such ensembles of opportunity is not justified. Key Points Models by the same institution behave similarly Structural model uncertainty is important Model development resembles an evolutionary process

The record‐breaking global mean surface temperature (GMST) in 2023 and 2024 came as a surprise to the scientific community, raising the question whether it provides evidence for a recent abrupt increase in the forced global warming rate. Here, we provide a new statistical learning‐based method to quantify the forced and internal variability contributions to annual GMST based on CMIP6‐simulated surface temperatures, producing a variability‐adjusted GMST time series. We find a variability contribution to 2023 GMST of 0.1 K, with strong contributions from the El Niño Southern Oscillation region and North Atlantic. More than half of the 2022–2023 jump in temperature is explained by variability, largely owing to anomalously cool conditions in 2022. We find insufficient evidence of an abrupt increase in forced warming rate in recent years. Our results highlight the importance of variability originating outside the tropical Pacific and the need to filter out unforced variability when assessing changes in long‐term warming rates.

This study investigates uncertainties in impact assessments when using climate projections. The uncertainties in health-related metrics combining temperature and humidity are much smaller than if the uncertainties in the two variables were independent. The finding reveals the potential for joint assessment of projection uncertainties in other variables used in impact studies. Impacts of climate change such as the effects on human discomfort, morbidity and mortality often depend on multiple climate variables. Thus, a comprehensive impact assessment is challenging and uncertainties in all contributing variables need to be taken into account. Here we show that uncertainties in some impact-relevant metrics such as extremes of health indicators are substantially smaller than generally anticipated. Models that project greater warming also show a stronger reduction in relative humidity. This joint behaviour of uncertainties is particularly pronounced in mid-continental land regions of the subtropics and mid-latitudes where the greatest changes in heat extremes are expected. The uncertainties in health-related metrics combining temperature and humidity are much smaller than if uncertainties in the two variables were independent. Such relationships also exist under present-day conditions where the effect of model biases in temperature and relative humidity largely cancel for combined quantities. Our results are consistent with thermodynamic first principles. More generally, the findings reveal a large potential for joint assessment of projection uncertainties in different variables used in impact studies.

Recent temperature extremes have shattered previously observed records, reaching intensities that were inconceivable before the events. Could the possibility of an event with such unprecedented intensity as the 2021 Pacific Northwest heatwave have been foreseen, based on climate model information available before the event? Could the scientific community have quantified its potential intensity based on the current generation of climate models? Here, we demonstrate how an ensemble boosting approach can be used to generate physically plausible storylines of a heatwave hotter than observed in the Pacific Northwest. We also show that heatwaves of much greater intensities than ever observed are possible in other locations like the Greater Chicago and Paris regions. In order to establish confidence in storylines of ‘black swan’-type events, different lines of evidence need to be combined along with process understanding to make this information robust and actionable for stakeholders. Climate model ensemble boosting can yield physically coherent storylines for record-shattering climate extremes such as the 2021 Pacific Northwest heatwave. Combining information from storyline approaches with process understanding can inform planning for future extremes of unprecedented intensity.

In order to limit climate warming, CO 2 emissions must remain below fixed quota. An evaluation of past emissions suggests that at 2014 emissions rates, the total quota will probably be exhausted within the next 30 years. Efforts to limit climate change below a given temperature level require that global emissions of CO 2 cumulated over time remain below a limited quota. This quota varies depending on the temperature level, the desired probability of staying below this level and the contributions of other gases. In spite of this restriction, global emissions of CO 2 from fossil fuel combustion and cement production have continued to grow by 2.5% per year on average over the past decade. Two thirds of the CO 2 emission quota consistent with a 2 °C temperature limit has already been used, and the total quota will likely be exhausted in a further 30 years at the 2014 emissions rates. We show that CO 2 emissions track the high end of the latest generation of emissions scenarios, due to lower than anticipated carbon intensity improvements of emerging economies and higher global gross domestic product growth. In the absence of more stringent mitigation, these trends are set to continue and further reduce the remaining quota until the onset of a potential new climate agreement in 2020. Breaking current emission trends in the short term is key to retaining credible climate targets within a rapidly diminishing emission quota.