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The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated1–3. Although various physical processes4–8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern9,10. In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area 9 . As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs 2 and a drifting buoys based Arctic SAT dataset 11 through employing the ‘data interpolating empirical orthogonal functions’ method 12 . Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR5 1 , for 1998–2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown. The Arctic is under-represented in surface temperature datasets and this could affect estimates of global warming. A new dataset with greater coverage of the Arctic shows a higher warming rate of 0.112 °C per decade compared to 0.005 °C from IPCC AR5.

The extreme monthly-mean temperatures simulated by 28 models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated and compared with those from 24 models in the third phase of the Coupled Model Intercomparison Project (CMIP3). Comparisons with observations and reanalyses indicate that the models from both CMIP3 and CMIP5 perform well in simulating temperature extremes, which are expressed as 20-yr return values. When the climatological annual cycle is removed, the ensemble spread in CMIP5 is smaller than that in CMIP3. Benefitting from a higher resolution, the CMIP5 models perform better at simulating extreme temperatures on the local gridcell scale. The CMIP5 representative concentration pathway (RCP4.5) and CMIP3 B1 experiments project a similar change pattern in the near future for both warm and cold extremes, and the pattern is in agreement with that of the seasonal extremes. By the late twenty-first century, the changes in monthly temperature extremes projected under the three CMIP3 (B1, A1B, and A2) and two CMIP5 (RCP4.5 and RCP8.5) scenarios generally follow the changes in climatological annual cycles, which is consistent with previous studies on daily extremes. Compared with the CMIP3 ensemble, the CMIP5 ensemble shows a larger intermodel uncertainty with regard to the change in cold extremes in snow-covered regions. Enhanced changes in extreme temperatures that exceed the global mean warming are found in regions where the retreat of snow (or the soil moisture feedback effect) plays an important role, confirming the findings for daily temperature extremes.

An overview of basic research on climate change in recent years in China is presented. In the past 100 years in China, average annual mean surface air temperature (SAT) has increased at a rate ranging from 0.03°C (10 yr)−1 to 0.12°C (10 yr)−1. This warming is more evident in northern China and is more significant in winter and spring. In the past 50 years in China, at least 27% of the average annual warming has been caused by urbanization. Overall, no significant trends have been detected in annual and/or summer precipitation in China on a whole for the past 100 years or 50 years. Both increases and decreases in frequencies of major extreme climate events have been observed for the past 50 years. The frequencies of extreme temperature events have generally displayed a consistent pattern of change across the country, while the frequencies of extreme precipitation events have shown only regionally and seasonally significant trends. The frequency of tropical cyclone landfall decreased slightly, but the frequency of sand/dust storms decreased significantly. Proxy records indicate that the annual mean SAT in the past a few decades is the highest in the past 400–500 years in China, but it may not have exceeded the highest level of the Medieval Warm Period (1000–1300 AD). Proxy records also indicate that droughts and floods in eastern China have been characterized by continuously abnormal rainfall periods, with the frequencies of extreme droughts and floods in the 20th century most likely being near the average levels of the past 2000 years. The attribution studies suggest that increasing greenhouse gas (GHG) concentrations in the atmosphere are likely to be a main factor for the observed surface warming nationwide. The Yangtze River and Huaihe River basins underwent a cooling trend in summer over the past 50 years, which might have been caused by increased aerosol concentrations and cloud cover. However, natural climate variability might have been a main driver for the mean and extreme precipitation variations observed over the past century. Climate models generally perform well in simulating the variations of annual mean SAT in China. They have also been used to project future changes in SAT under varied GHG emission scenarios. Large uncertainties have remained in these model-based projections, however, especially for the projected trends of regional precipitation and extreme climate events.

This paper examines the capability of three regional climate models (RCMs), i.e., RegCM3 (the International Centre for Theoretical Physics Regional Climate Model), PRECIS (Providing Regional Climates for Impacts Studies) and CMM5 (the fifth-generation Pennsylvania State University-the National Center for Atmospheric Research of USA, NCAR Mesoscale Model) to simulate the near-surface-layer winds (10 m above surface) all over China in the late 20th century. Results suggest that like global climate models (GCMs), these RCMs have the certain capability of imitating the distribution of mean wind speed and fail to simulate the greatly weakening wind trends for the past 50 years in the country. However, RCMs especially RegCM3 have the better capability than that of GCMs to simulate the distribution and change feature of mean wind speed. In view of their merits, these RCMs were used to project the variability of near-surface-layer winds over China for the 21st century. The results show that 1) summer mean wind speed for 2020–2029 will be lower compared to those in 1990–1999 in most area of China; 2) annual and winter mean wind speed for 2081–2100 will be lower than those of 1971–1990 in the whole China; and 3) the changes of summer mean wind speed for 2081–2100 are uncertain. As a result, although climate models are absolutely necessary for projecting climate change to come, there are great uncertainties in projections, especially for wind speed, and these issues need to be further explored.

This article summarizes the main results and findings of studies conducted by Chinese scientists in the past five years. It is shown that observed climate change in China bears a strong similarity with the global average. The country-averaged annual mean surface air temperature has increased by 1.1°C over the past 50 years and 0.5–0.8°C over the past 100 years, slightly higher than the global temperature increase for the same periods. Northern China and winter have experienced the greatest increases in surface air temperature. Although no significant trend has been found in country-averaged annual precipitation, interdecadal variability and obvious trends on regional scales are detectable, with northwestern China and the mid and lower Yangtze River basin having undergone an obvious increase, and North China a severe drought. Some analyses show that frequency and magnitude of extreme weather and climate events have also undergone significant changes in the past 50 years or so. Studies of the causes of regional climate change through the use of climate models and consideration of various forcings, show that the warming of the last 50 years could possibly be attributed to an increased atmospheric concentration of greenhouse gases, while the temperature change of the first half of the 20th century may be due to solar activity, volcanic eruptions and sea surface temperature change. A significant decline in sunshine duration and solar radiation at the surface in eastern China has been attributed to the increased emission of pollutants. Projections of future climate by models of the NCC (National Climate Center, China Meteorological Administration) and the IAP (Institute of Atmospheric Physics, Chinese Academy of Sciences), as well as 40 models developed overseas, indicate a potential significant warming in China in the 21st century, with the largest warming set to occur in winter months and in northern China. Under varied emission scenarios, the country-averaged annual mean temperature is projected to increase by 1.5–2.1°C by 2020, 2.3–3.3°C by 2050, and by 3.9–6.0°C by 2100, in comparison to the 30-year average of 1961–1990. Most models project a 10%–12% increase in annual precipitation in China by 2100, with the trend being particularly evident in Northeast and Northwest China, but with parts of central China probably undergoing a drying trend. Large uncertainty exists in the projection of precipitation, and further studies are needed. Furthermore, anthropogenic climate change will probably lead to a weaker winter monsoon and a stronger summer monsoon in eastern Asia.

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951–1980) exceeding 3σ (σ is based on the local internal variability) are defined as “extremely hot”. The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, “extremely hot” summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, “extremely hot” summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by “extremely hot” summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low.

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed "MDVM", which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30-90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850-2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950-1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450-1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data.

The signing of the UN Framework Convention on Climate Change in Rio de Janeiro in June 1992 by 160 nations has firmly identified global climate change due to human pollution as a pressing global environmental concern. Among the responsibilities that the nations which ratify the Convention will have are the drawing up of inventories of greenhouse gas sources and sinks and the formulation of national strategies to respond to climate change through adaptive and or preventive measures. One requirement for identifying appropriate response strategies will be the undertaking of regional assessments of climate change and its associated impacts. This paper is concerned with climate change in the East Asian region, both over the last 100 years (using instrumental data) and also for the next 100 years (using results from climate model experiments). The juxtaposing of these two analyses, historical and future, enables a better interpretation of the significance of regional climate change to be made. Instrumental temperature and precipitation data for the East Asian region are analysed and compared with the observed global-scale trends in these two variables. Although the region has undoubtedly warmed over the last century, understanding the exact causes of the complex seasonal, diurnal, and spatial dimensions of this warming is difficult. We examine the role of increasing urbanization in inducing rising temperatures and suggest that, although substantial, urban warming cannot account for all of the observed temperature change. The paper also illustrates a flexible composite-model approach to regional climate change scenario construction that avoids the need for multiple transient GCM experiments, which can explicitly incorporate the effects of intermodel uncertainty, and is flexible enough to incorporate new scientific findings and results from new GCM experiments. The scenario presented here suggests that by 2050, mean conditions are expected to be warmer than the extremely warm seasonal anomalies that occurred during the most recent decade in East Asia. Precipitation is estimated to rise over most of the region in all seasons, although the uncertainty range attached to this estimate is much wider than for temperature.

As we know, the projections of future climate change including impacts and strategies in the IPCC Assessment Reports were based on global climate models with scenarios on various human activities. Global climate model simulations provide key inputs for climate change assessments. In this study, the main objective is to analyze if the projections of future climate change by global climate models are reliable. Several workshops have been held on this issue, such as the IPCC expert meeting on assessing and combining multi-model climate projections in January of 2010 (presided by the co-chairs of the IPCC WGI and WGII AR5), and the workshop of the combined global climate model group held by NCAR in June of 2010. Many modeling experts and users took part in those workshops. These series of workshops and conferences on climate models investigated several key issues on advances in determining the reliability of future climate change projections. The description and analysis of the workshop results are intended as guidance for future climate scientists and for impact and adaptation studies by using the results from model inter-comparison projects on global and regional climate change.