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This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ∼7% °C−1. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.

This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.

Linear trend analysis is commonly applied to quantify sea level change, often over short periods because of limited data availability. However, the linear trend computed over short periods is complicated by large‐scale climate variability which can affect regional sea level on interannual to inter‐decadal time scales. As a result, the meaning of a local linear sea level trend over the short altimeter era (since 1993; less than 20 years) is unclear, and it is not straightforward to distinguish the regional sea level changes associated with climate change from those associated with natural climate variability. In this study, we use continuous near‐global altimeter measurements since 1993 to attempt to separate interannual and decadal sea level variability in the Pacific from the sea level trend. We conclude that the rapid rates of sea level rise in the western tropical Pacific found from a single variable linear regression analysis are partially due to basin‐scale decadal climate variability. The negligible sea level rise, or even falling sea level, in the eastern tropical Pacific and US west coast is a result of the combination of decreasing of sea level associated with decadal climate variability and a positive sea level trend. The single variable linear regression analysis only accounts for slightly more than 20% of the observed variance, whereas a multiple variable linear regression including filtered indices of the El Nino‐Southern Oscillation and the Pacific Decadal Oscillation accounts for almost 60% of the observed variance. Key Points Sea level linear trend over short period is complicated by climate variability We separate interannual and decadal sea level variability from trend in Pacific Decadal sea level variability can be erroneously aliased into sea level trend

The contribution of urbanization to warming in China has been difficult to quantify owing to the proximity of rural stations to urban areas. A novel detection and attribution analysis separates the contribution of all external forcings, and shows that urbanization accounts for about one-third (0.5 °C) of the total warming signal in China (1.4 °C). China has warmed rapidly over the past half century 1 and has experienced widespread concomitant impacts on water availability, agriculture and ecosystems 2 . Although urban areas occupy less than 1% of China’s land mass, the majority of China’s observing stations are situated in proximity to urban areas, and thus some of the recorded warming is undoubtedly the consequence of rapid urban development, particularly since the late 1970s 3 , 4 , 5 . Here, we quantify the separate contributions of urbanization and other external forcings to the observed warming. We estimate that China’s temperature increased by 1.44 °C (90% confidence interval 1.22–1.66 °C) over the period 1961–2013 and that urban warming influences account for about a third of this observed warming, 0.49 °C (0.12–0.86 °C). Anthropogenic and natural external forcings combined explain most of the rest of the observed warming, contributing 0.93 °C (0.61–1.24 °C). This is close to the warming of 1.09 °C (0.86–1.31 °C) observed in global mean land temperatures over the period 1951–2010, which, in contrast to China’s recorded temperature change, is only weakly affected by urban warming influences 6 . Clearly the effects of urbanization have considerably exacerbated the warming experienced by the large majority of the Chinese population in comparison with the warming that they would have experienced as a result of external forcing alone.

This paper presents projected changes in temperature and precipitation extremes in China by the end of the twenty-first century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. The temporal changes and their spatial patterns in the Expert Team on Climate Change Detection and Indices (ETCCDI) indices under the RCP4.5 and RCP8.5 emission scenarios are analyzed. Compared to the reference period 1986–2005, substantial changes are projected in temperature and precipitation extremes under both emission scenarios. These changes include a decrease in cold extremes, an increase in warm extremes, and an intensification of precipitation extremes. The intermodel spread in the projection increases with time, with wider spread under RCP8.5 than RCP4.5 for most indices, especially at the subregional scale. The difference in the projected changes under the two RCPs begins to emerge in the 2040s. Analyses based on the mixed-effects analysis of variance (ANOVA) model indicate that by the end of the twenty-first century, at the national scale, the dominant contributor to the projection uncertainty of most temperature-based indices, and some precipitation extremes [including maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day), and total extremely wet day total amount (R95p)], is the difference in emission scenarios. By the end of the twenty-first century, model uncertainty is the dominant factor at the regional scale and for the other indices. Natural variability can also play very important role.

Global climate models project the intensification of marine heatwaves in coming decades due to global warming. However, the spatial resolution of these models is inadequate to resolve mesoscale processes that dominate variability in boundary current regions where societal and economic impacts of marine heatwaves are substantial. Here we compare the historical and projected changes in marine heatwaves in a 0.1° ocean model with 23 coarser-resolution climate models. Western boundary currents are the regions where the models disagree the most with observations and among themselves in simulating marine heatwaves of the past and the future. The lack of eddy-driven variability in the coarse-resolution models results in less intense marine heatwaves over the historical period and greater intensification in the coming decades. Although the projected changes agree well at the global scale, the greater spatial details around western boundary currents provided by the high-resolution model may be valuable for effective adaptation planning. Marine heatwaves are likely to intensify in a warmer world, but prediction of these events is hampered by course-scale modeling. Here the authors develop a fine scale, global model which shows that marine heatwaves will amplify with greater spatial variability, particularly at western boundary regions.

This paper examines new evidence from observational and detection and attribution studies of changes in extreme precipitation in China since the early 1960s. We have also designed a series of sensitivity tests to explore the robustness of detection and attribution results to the differences in sample size, in extreme precipitation index, and in data processing procedure. Our analyses used the most recent update of observational records as well as simulations conducted with the climate models participated in the Coupled Model Intercomparison Project Phase 6. Based on the existing studies and our additional analyses, we found that human influence is detectable in extreme precipitation in China regardless of the period, extreme precipitation index, or data treatment considered, in both China as a whole and in northern and southern China separately. We also found, as is often encountered in detection and attribution studies, it is difficult to separate the contribution from anthropogenic forcing from that of natural external forcing, and it is also challenging to decompose the anthropogenic component into a greenhouse gas forcing component and a component that reflects other anthropogenic forcing agents (dominantly, aerosols).

Changes in European river flow have amplified the dry-south–wet-north contrast. Model simulations show that anthropogenic climate change accounts for this change with strong decreases in the Mediterranean and weak increases in northern Europe. Although there is overwhelming evidence showing that human emissions are affecting a wide range of atmospheric variables 1 , it is not clear whether anthropogenic climate change is detectable in continental-scale freshwater resources. Owing to the complexity of terrestrial hydro-systems there is to date only limited evidence suggesting that climate change has altered river discharge in specific regions 2 , 3 , 4 , 5 . Here we show that it is likely 6 that anthropogenic emissions have left a detectable fingerprint in renewable freshwater resources in Europe. We use the detection and attribution approach 1 , 7 to compare river-flow observations 8 with state-of-the-art climate model simulations 9 . The analysis shows that the previously observed amplification of the south (dry)–north (wet) contrast in pan-European river flow 10 is captured by climate models only if human emissions are accounted for, although the models significantly underestimate the response. A regional analysis highlights that a strong and significant decrease is observed in the Mediterranean, generally along with a weak increase in northern Europe, whereas there is little change in transitional central Europe. As river and streamflow are indicators for renewable freshwater resources 11 , 12 , 13 , the results highlight the necessity of raising awareness on climate change projections 5 , 14 that indicate increasing water scarcity in southern Europe.

Extreme cold surges, very large temperature drops over a short period of time, have serious impacts on human health, energy supply and ecosystems. While changes in temperature variability and cold extremes in a warming climate are well understood, changes in extreme cold surges and their driving mechanisms are not. Here we show that extreme cold surges have robustly weakened in middle-to-high latitude continents during autumn and winter but have remained almost unchanged in lower latitudes. By diagnosing near-surface thermodynamic budget, we find that this change is mainly induced by anthropogenic forcing. Greenhouse gas forcing decreases the meridional temperature gradient and associated variability in middle-to-high latitudes but has minimal impact in lower latitudes. This leads to similar spatial pattern of changes in nonlinear horizontal temperature advection that dominantly drives the extreme cold surges. Influenced by the same mechanism, extreme cold surges during winter across middle-to-high latitudes will continue to weaken in the future, with an 8%−13% reduction in their strength by the end of the century under the SSP 3-7.0 scenario. This study shows that human-induced warming has weakened extreme cold surges in middle-to-high latitudes but has had minimal effect in lower latitudes. This is due to reduced north-south temperature differences and this pattern of change is projected to persist with future warming.

TNFAIP8 family molecules have been recognized for their involvement in the progression of tumors across a range of cancer types. Emerging experimental data suggests a role for certain TNFAIP8 family molecules in the development of glioma. Nonetheless, the comprehensive understanding of the genomic alterations, prognostic significance, and immunological profiles of TNFAIP8 family molecules in glioma remains incomplete. In the study, using the comprehensive bioinformatics tools, we explored the unique functions of 4 TNFAIP8 members including TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 in glioma. The expressions of TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 were notably upregulated in glioma tissues compared to normal tissues. Furthermore, survival analysis indicated that elevated expression levels of TNFAIP8, TNFAIP8L1 and TNFAIP8L2 were correlated with unfavorable outcomes in terms of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) among glioma patients. Genetic modifications, such as mutations and copy number alterations, within the TNFAIP8 family exhibited a significant association with extended OS, DSS and PFS in individuals diagnosed with glioma. The findings suggest a noteworthy correlation between TNFAIP8 family members and the age and 1p/19q codeletion status of glioma patients. We also found that there were significant relationships between TNFAIP8 family expression and tumor immunity in glioma. Furthermore, functional annotation of TNFAIP8 family members and their co-expressed genes in gliomas was carried out using GO and KEGG pathway analysis. The GO analysis revealed that the primary biological processes influenced by the TNFAIP8 family co-expressed genes included cell chemotaxis, temperature homeostasis, and endocytic vesicle formation. Additionally, the KEGG analysis demonstrated that TNFAIP8 family co-expressed genes are involved in regulating various pathways such as inflammatory mediator regulation of TRP channels, pathways in cancer, prolactin signaling pathway, and Fc gamma R-mediated phagocytosis. Overall, the findings suggest that TNFAIP8 family members may play a significant role in the development of glioma and have the potential to serve as prognostic indicators and therapeutic targets for individuals with glioma.