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Article

Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models

by
Yuxi Jiang
1,2,
Yutao Chi
1,2,
Weidong Wang
1,2,
Wenshan Li
1,2,*,
Hui Wang
1,2 and
Jianxiang Sun
3
1
National Marine Data and Information Service, Tianjin 300171, China
2
Observation and Research Station of Bohai-rim Sea Level Change and Coastal Erosion, Ministry of Natural Resources, Tianjin 300171, China
3
Tianjin Navigation Instrument Research Institute, Tianjin 300131, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(10), 1143; https://doi.org/10.3390/atmos16101143
Submission received: 30 May 2025 / Revised: 19 August 2025 / Accepted: 19 August 2025 / Published: 29 September 2025
(This article belongs to the Section Climatology)
Browse Figures
Versions Notes

Abstract

Global warming has been altering the East Asian climate at an unprecedented rate since the 20th century. In order to evaluate the changes in the East Asian winter climate (EAWC) and support policy-making for climate mitigation and adaptation strategies, this paper utilizes the multimodel ensemble from the Couple Model Intercomparison Project 6 and a temperature threshold method to investigate the EAWC changes during the period 1979–2100. The results show that the EAWC has been undergoing widespread and robust changes in response to global warming. The winter length in East Asia has shortened and will continue shortening owing to later onsets and earlier withdrawals, leading to a drastic contraction in length from 100 days in 1979 to 43 days (27 days) in 2100 under SSP2-4.5 (SSP5-8.5). While most regions of the East Asian continent are projected to become warmer in winter, the Japan and marginal seas of northeastern Asia will face the risks from colder winters with more frequent extreme cold events, accompanied by less precipitation. Meanwhile, the Tibetan Plateau is very likely to have colder winters in the future, though its surface snow amounts will significantly decline. Greenhouse gas (GHG) emissions are found to be responsible for the EAWC changes. GHG traps heat inside the Earth’s atmosphere and notably increases the air temperature; moreover, its force modulates large-scale atmospheric circulation, facilitating an enhanced and northward-positioned Aleutian low together with a weakened Siberian high, East Asian trough, and East Asian jet stream. These two effects work together, resulting in a contracted winter with robust and uneven regional changes in the EAWC. This finding highlights the urgency of curbing GHG emissions and improving forecasts of the EAWC, which are crucial for mitigating their major ecological and social impacts.

1. Introduction

East Asia, as one of the most densely populated and industrialized regions in the world, supported the livelihood of over 1.6 billion people in 2024, accounting for one-fifth of the world’s population [1,2]. Its livability is very sensitive and susceptible to the East Asian winter climate (EAWC); in some circumstances, an anomalous state of the EAWC can cause disastrous weather and enormous socioeconomic losses to East Asia [3,4]. For example, in 2008, a massive ice storm contributed to extremely cold temperatures, heavy snowfall, and freezing precipitation in China, causing direct economic losses of more than USD 22.8 billion and 129 deaths [5]. The EAWC is also reported to have profound effects on human health and livelihood [6,7], vulnerable ecosystems [8,9,10], hydrology [11], and wildfires [12]. For instance, cold can increase cardiovascular mortality by up to 54.72%, while winter warming prolongs the growing season and changes plant community composition, favoring productive species and subsequent biomass production and leading to diversity losses and changes in hydrological systems [6,7,8,9,10,11]. Given the significant role that the winter climate plays in East Asia, it is of great importance to understand the changes to the EAWC and its underlying mechanisms, as they can pose severe threats to the region.
The EAWC is significantly influenced by the state of the East Asian winter monsoon (EAWM). In general, when the EAWM is stronger (weaker), East Asia tends to be colder (warmer) and drier (wetter) in winter, with more (fewer) outbreaks of snowfall in northern China and freezing rain in southeastern China [7]. Previous studies have shown that the EAWM varies prominently on the annual and decadal time scales. On the annual time scale, many factors are found to have impacts on the EAWM and associated EAWC, such as the Arctic Oscillation (AO) [13], El Niño/Southern Oscillation (ENSO) [14], Kuroshio Extension variability [15], North Atlantic Oscillation (NAO) [16], autumn Eurasian snow cover, and Arctic sea ice [17,18], all of which lead to the significant annual variability of the EAWC. Both the unprecedented freezing disaster in 2008 and warm winter in 2016/2017 are considered to have been largely driven by some of these factors [19,20]. At the decadal time scale, the EAWM shows an abrupt weakening around the mid-1980s, which is largely attributed to both natural forces (e.g., the AO, the Pacific Decadal Oscillation) [21] and external forces (e.g., anthropogenic aerosols and greenhouse effects) [22,23]. As a result, winters in East Asia have shown an overall warming trend since the mid-1980s. In addition, global warming is another predominant factor leading to the long-term variability of the EAWC, as it can directly provide warmer context and indirectly influence the EAWC through modulating the EAWM [21].
Increasing evidence suggests that, regulated by the above driving factors, the EAWM and associated EAWC have displayed distinct changes in recent decades and will continue to change in the future [7,24,25]. The EAWM weakened around the mid-1980s and is projected to become even weaker in a future warmer climate based on the Coupled Model Intercomparison Project phase 5 (CMIP5) [7,24], favoring a warmer East Asia in winter with the surface air temperature (SAT) increasing by 3 °C under RCP4.5 and 5.5 °C under RCP8.5 towards the end of the 21st century [25]. Meanwhile, as the global mean temperature has risen, the initial phases of the four seasons have shifted remarkably, and the ongoing shortening of winter length will be further exacerbated in the future [26]. However, previous studies mainly obtained projections of the East Asian climate within the framework of CMIP5, which may introduce considerable uncertainty into the results. Compared with CMIP5, the current Couple Model Intercomparison Project phase 6 (CMIP6) models are significantly improved in terms of resolution, physical processes, and overall performance [27,28,29,30,31], which drove us to explore future EAWC changes under the CMIP6 framework. In addition, while existing studies still regard December–February as winter under a warming climate, a more precise and detailed analysis for winter is lacking owning to seasonal cycle changes.
Therefore, the objectives of this study are as follows: first, to assess the historical and future responses of EAWC to climate change using CMIP6 multimodel results from the perspectives of winter onsets, lengths, and crucial climatic variables; second, to investigate the underlying physical mechanisms through which climate change modulates the EAWC. The data and methods used in the present study are described in Section 2. Section 3 shows the results. In Section 4, we summarize the results. Section 5 is the discussion.

2. Data and Methods

2.1. Data

CMIP6 is used to obtain multimodel ensemble simulations for the period of 1979–2100, including historical climate simulations (1979–2014) and climate projections (2015–2100) under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 forcing scenarios. Here, for SSPx-y, SSP represents the shared socioeconomic pathway, and x and y represent the specific SSP and forcing pathway, respectively. Specifically, SSP1-2.6 is a low forcing and sustainable emission pathway with potential future growth of green energy [32], SSP2-4.5 represents a “middle-of-the-road” scenario [33], and SSP5-8.5 is the high end of the future forcing pathway with fossil-fueled growth [34]. A total of nine CMIP6 models are selected in this study (Table 1), considering these models have been widely used in relevant research and proven to be reliable in investigating climate changes [26,35,36,37]. To assess the relative contributions of anthropogenic aerosols, greenhouse gases (GHG), and natural forcing to the winter climate changes in East Asia, four subsets of historical simulations are analyzed in this study. These four historical runs are an all-forcing run, aerosol-forcing run, GHG-forcing run, and natural forcing run, named His, His-aer, His-GHG, and His-nat, respectively, hereafter. For each model, the variant named “r1i1p1f1” (r: realization; i: initialization; p: physics; f: forcing) is selected if it is available, and another variant with the smallest realization will be adopted as the substitute if “r1i1p1f1” is inaccessible.
The monthly mean reanalysis datasets from two sources are employed as observational data to evaluate the performance of CMIP6 models, including the fifth major global reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5) with a 31 km horizontal resolution [38] and the Reanalysis-1 product from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) on a 2.5° × 2.5° grid [39]. For a fair comparison, all models’ output and observed data are linearly interpreted to the 2.5° latitude × 2.5° longitude grid [40]. In this study, East Asia refers to the region of 20–60° N and 70–150° E (Figure 1).

2.2. Methods

The skill score, proposed by [41], is calculated to evaluate the nine CMIP6 models on their spatial performance, because it is one of the least complicated scores combining the spatial pattern and magnitude together and has been applied in assessing model performance in previous studies [42,43]. The formula for the skill score is given as below:
S k i l l   S c o r e = 4 ( 1 + R f r ) 4 ( S f r + 1 S f r ) 2 ( 1 + R 0 ) 4 ,
where R f r is the spatial correlation coefficient between the observed and model distributions, S f r is the ratio of the model’s pattern standard deviation to the observed pattern standard deviation, and R 0 is the maximum correlation, which is equal to 1 in this case. Another indicator used is the temporal correlation coefficient between models and observations, which is employed to measure the model’s capacity for reproducing temporal variations.
The method used for defining winter is listed below: (1) the variables on 29 February in leap years are omitted to restrict each year to 365 days, which has little effect on long-term climatic trends; (2) to eliminate the effects of day-to-day fluctuations on the onsets and withdrawals of winter, a Savitzky–Golay filter with a window length of 91 days and a polynomial of one degree is applied to the raw data to remove daily variability (Figure 2); (3) following [26,44], the local temperature threshold method is adopted to define seasons in this study, in which winter starts when the daily SAT is below the 25th percentile of SAT averaged over 1979–2014 and ends when the daily SAT exceeds the 25th percentile. By applying the temperature threshold definition, a total of 35 and 85 winters are detected in the historical simulations and future projections, respectively. In this study, the winter of a given year denotes the winter starting late that year or early the following year and ending early the following year.
The Aleutian low (AL) index, represented by the area-weighted average of winter sea-level pressure (SLP) over the region bounded by 40–70° N and 160° E–160° W, is used to reflect the intensity and meridional position of the AL. The East Asian trough (EAT) intensity is defined as the average 500 hPa geopotential height at 125–145° E and 35–50° N [15]. Following [24], the difference of average 300 hPa zonal wind between (27.5–37.5° N, 110–170° E) and (50–60° N, 80–140° E) is applied to represent the East Asian jet stream (EAJS) intensity. The significances of long-term trends are estimated using a two-tailed Student’s t-test.

3. Results

3.1. Selection of CMIP6 Models

To obtain a more reliable description of winter climate change in East Asia, we evaluate the performance of nine CMIP6 models by comparing their simulated spatial patterns, intensities, and temporal variations in key atmospheric variables against the observations. The time period used for assessment here is December, January, and February (DJF) during 1979–2014, which is regarded as the conventional winter season in East Asia [15,45]. SAT, SLP, and 500 hPa geopotential height are chosen as key indicators of the model performance’s evaluation, considering they are the representative variables of the EAWM, Siberian high (SH)/AL, and EAT, respectively, all of which are major components of the East Asian winter climate system [4,45,46].
As displayed in Figure 3, all CMIP6 models gain high skill scores in reproducing SAT and 500 hPa geopotential height, with average skill scores of 0.98 and 0.99, respectively, compared with the observational datasets. However, the models exhibit a comparatively lower performance in simulating SLP, with an average score of 0.76 (0.78) compared against ERA5 (NCEP-NCAR Reanalysis-1). Notably, MIROC6 underperforms in capturing the spatial distribution of SLP compared to the other models. Table 2 further shows each model’s capability of reproducing temporal variations, suggesting that NorESM2-LM has a relatively poor performance in capturing all three variables.
In summary, seven of the nine CMIP6 models—excluding MIROC6 and NorESM2-LM—are capable of realistically reproducing the main features of the EAWC. Therefore, these seven models are selected to calculate the multimodel ensemble with equal weighting in the following.

3.2. Observed and Projected Changes in Winter Climate

Figure 4 presents the climatological state of winters during the 1979–2014 period. For most of the East Asian continent, winter typically started in late November, and its start date exhibited a delay trend when moving toward the ocean, with the average winter onset in Japan being on the 4th and 5th pentad of December (Figure 4a). Similarly, winter withdrawals also displayed a robust land–sea contrast with a delay trend seaward (Figure 4b), resulting in a basically even winter length of 85 to 95 days in East Asia (Figure 4c). As shown in Figure 4d, winter SAT varied widely, ranging from below −20 °C in northern East Asia and the Tibetan Plateau to 25 °C in southern East Asia.
Under the background of climate change, the winter state in East Asia experienced noticeable changes. From 1979 to 2014, the average winter onset in the study area delayed by 3.3 days per decade, with a more prominent delay over the ocean and the Tibetan Plateau, reaching rates of 7 days·decade−1 and 4 days·decade−1, respectively (Figure 5a). The later starts, together with earlier ends of winter, led to a widespread shortening of winter length at an average rate of 5.9 days·decade−1 (Figure 6a). As the winter duration shrank, the SAT increased by 0.3 °C per decade, with the most significant warming observed between 40 and 50° N (Figure 7a). A warmer East Asia is found to be closely related to climate warming. Under global warming, enhanced heat transport by western boundary currents in the North Pacific gives rise to a warmer North Pacific at mid-latitudes, and thereby leads to a warming Eurasia via the weakened EAT and subtropical jet through barotropic responses to the warm North Pacific. In addition to climate change, internal variability also plays a considerable role in inducing the observed winter temperature trends [4,26,47].
In the future (2015–2100), even under the most optimistic scenario (SSP1-2.6), winter in East Asia will continuously start later and become shorter, by 0.6 days and 1.3 days per decade, respectively (Figure 5b and Figure 6b). Meanwhile, the temperature will decrease over the Tibetan Plateau, Japan, and most oceanic areas and increase over the remaining areas (Figure 7b). Under the stabilization scenario (SSP2-4.5), winter onset is projected to delay at a rate of 1.7 days per decade and winter length will shrink by 3.5 days per decade (Figure 5c and Figure 6c), accompanied by a more prominent pattern of temperature trends compared to SSP1-2.6 (Figure 7c). The results from the most aggressive scenario (SSP5-8.5) show that almost the entirety of East Asia will undergo the most robust changes with later winter starts (2.8 days·decade−1) and shorter winter lengths (5.7 days·decade−1) (Figure 5d and Figure 6d), but the temperature trends exhibit distinct regional differences. While the winter temperature over the continent (except the Tibetan Plateau) is projected to increase at an average rate of 0.3 °C·decade−1, the temperature over the Tibetan Plateau, Japan, and most oceanic areas will decrease, with the most pronounced decrease rate of 0.8 °C per decade located over the Okhotsk Sea (Figure 7d). In all the three scenarios, the winter shortening trend is attributed to both later starts and earlier ends, both of which approximately contribute to half of the winter length reduction.
As winter contracted, on average, the winter start date delayed by 11 days from 2 December in 1979 to 13 December in 2013, and it will continue to delay to 23 December, 1 January, and 7 January by the winter of 2099 under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. In contrast, the winter end date advanced by 10 days from 11 March in 1979 to 1 March in 2013, and it is projected to keep on advancing to 19 February, 12 February, and 2 February by the winter of 2099 under the same three scenarios (Figure 8a). The shifts in onsets and withdrawals contribute to a pronounced shortening of winter length. As a result, while the winter duration was 100 days in 1979 and decreased to 79 days in 2013, there is good potential for a 59-day, 43-day, and 27-day winter in 2099 under the three scenarios, respectively (Figure 8b).
It is noteworthy that the shortening rate of winter length under the SSP5-8.5 closely follows the path of historical simulations, which means the EAWC will continuously deteriorate at the previous rate if GHG emissions are not curbed, alarming us that we will face high risks of agriculture, ecology, energy, and human health if we do not take any effective measures [9,48,49,50]. The situation may be more serious in urban areas, as the urban heat island effect can further exacerbate thermal stress, potentially resulting in more robust EAWC changes compared to the suburbs [51].
In addition, high GHG emissions are projected to lead to the disappearance of winter in certain regions in the future. Under the business-as-usual scenario, the winter is forecasted to vanish in some regions of the Okhotsk Sea from around 2060–2080 (Figure 9a). Under the high-emission scenario, winter will gradually disappear across some parts of eastern East Asia, with winter over the Okhotsk Sea first disappearing around 2050–2070, followed by the Sea of Japan, Sakhalin Island, and Hokkaido during 2080–2085, and the western North Pacific during 2080–2100 (Figure 9b). Winter disappearance is likely to disrupt biological rhythms, including the phenological cycles of plants [52], reproduction patterns of certain plants and insects, and bird migration behaviors [53], thereby disrupting the structure and function of ecological communities. Furthermore, the disappearance of winter will also bring great challenges to the tourism of Hokkaido, Japan, as Hokkaido is a highly snow-dependent skiing area, with the ski resort market size valued at USD 0.57 billion in 2024 [54,55].
Climate change not only shortens winter but also alters the winter climate state across East Asia, as depicted by Figure 10. The spatial patterns of the daily maximum and minimum SAT trends closely resemble the pattern of the SAT trend, implying widespread winter warming across much of East Asia, with higher daytime highs and nighttime lows, which is consistent with global warming [56]. In contrast, the Tibetan Plateau, Japan, and marginal seas of northeastern Asia will experience a colder winter with more frequent extreme cold events (Figure 7b–d and Figure 10a–f). This is expected to pose enormous challenges to disaster response and recovery for Japan, as the intensified cold air obtains more latent heat over the Sea of Japan, leading to extreme heavy snowfall events in Japan [57]. Furthermore, the different change amplitudes of daily maximum and minimum temperature result in projected changes in the diurnal temperature range (DTR). Under SSP2-4.5, the spatial pattern of DTR trend over East Asia is consistent with the results from [58] (Figure 10h). As the DTR trend under SSP5-8.5 shares a similar spatial distribution but with greater amplitudes, under SSP5-8.5, the DTR can increase at a rate as high as 0.5–0.7 °C·decade−1 over southwestern China and Japan, while declining by 0.4 °C per decade over northern East Asia (Figure 10i). Such rapid changes in DTR will have widespread impacts on agricultural productivity [59], ecosystems [60], and public health [61,62].
Figure 11 shows the projected trends of wintertime precipitation and surface snow amounts. The precipitation band extending from Taiwan to the Kuroshio–Oyashio Confluence Region is predicted to weaken (Figure 11a–c). Under the strongest future warming scenario, the region to the east of Japan is expected to experience a maximum reduction in precipitation of up to 0.4 mm·day−1 per decade, and by 2100, the intensity of the precipitation band near Japan is projected to decrease by approximately 20% compared to its 2015 level (Figure 12). Climate change will also reduce the surface snow amounts across East Asia, especially on the Tibetan Plateau, with the averaged surface snow amounts on the Tibetan Plateau declining rapidly from around 100 kg·m−2 in 2015 to around 20 kg·m−2 by the end of this century (Figure 11d–f and Figure 12). As a result, the plateau ecosystem will be on a more dangerous path as most glaciers on the Tibetan Plateau have already undergone continuous mass loss in response to global warming [63].

3.3. Possible Mechanisms

As the East Asian climate is directly linked with background temperature and EAWM, Figure 13 gives the long-term trends of SAT and important components of EAWM in DJF. In the historical simulations, the temperature shows a pronounced warming trend (Figure 13(a1)), accompanied by a deepening and northward shift of AL and weakening of SH in the lower troposphere (Figure 13(b1)), a shallower EAT in the middle troposphere (Figure 13(c1)), and a weaker meridional shear of the EAJS in the upper troposphere (Figure 13(d1)), all favoring a shorter and warmer winter in East Asia. It is found that the intensification and poleward shift of AL are driven by an El Niño-like sea surface temperature warming in the tropical Pacific and sea ice losses in the Bering Sea and the Sea of Okhotsk in response to greenhouse warming [25,64,65]. The future scenarios also reveal strong warming trends, which increase with latitude, ranging from 0~0.2 °C·decade−1 under SSP1-2.6 to 0.3~1.2 °C·decade−1 under SSP5-8.5 in East Asia (Figure 13(a2–a4)). The AL will also continue to strengthen and expand poleward, which is consistent with the projections from [64], and the SH, EAT, and EAJS will become weaker (Figure 13(b2–b4,c2–c4,d2–d4)), resulting in more contracted and warmer winter in most East Asian regions. It is noteworthy that, over the marginal seas of northeastern Asia, winter shows a remarkable cooling trend in the future (Figure 7b–d), possibly due to the acceleration of the low-level northerly winds associated with the AL change.
To find the relative contributions of various factors including GHG, anthropogenic aerosols, and natural forcing to the observed climate change, we compare multimodel mean trends of SAT and EAWM-related indices with the observations, as displayed in Figure 14. It shows that the GHG forcing rather than anthropogenic aerosols and natural variations is the principal driver of the historical warming trends (Figure 14a), providing a warmer background for East Asia, which is consistent with [26,36]. While the models run by only natural forcing show significant divergences on the variation trends of atmospheric circulations, the trends forced by well-mixed GHG are close to the observed trends, which account for 92.1%, 95.4%, and 35.7% of the observed AL, EAT, and EAJS changes, respectively. The contributions of other potential drivers to East Asian warming and EAWM-related circulation are small compared to GHG and, under some circumstances, other driving factors even exhibit negative contributions to climate change in East Asia (Figure 14b–d). These results suggest that GHG forcing dominates winter climate change in East Asia. Therefore, it is of great importance for us to restrict the GHG concentration to a moderate level; otherwise, it may have a severe influence on atmospheric circulation and thus the seasonal climate.

4. Conclusions

Widespread, rapid, and intensifying climate change, as one of the most serious global threats, has caused many of the observed changes that have been unprecedented for thousands of years [66]. This study investigates winter climate change in East Asia during 1979–2100 by using CMIP6 models including the historical simulations (1979–2014) and climate projections (2015–2100) under three shared socioeconomic scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5).
It is found that, during the period of 1979–2100, the averaged winter onset (withdrawal) is projected to be delayed (advanced) from 2 December (11 March) to 23 December, 1 January, and 7 January (19 February, 12 February, and 2 February) under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, resulting in winter lengths dramatically shrinking from 100 days to 59, 43, and 27 days, respectively. In the most aggressive scenario, winter over the eastern parts of East Asia even faces the risk of disappearance in the 21st century. The wintertime SAT displays an overall warming trend in historical simulations, while it is expected to increase by up to 3~4 °C over most continental regions of East Asia and decrease by up to 3~6 °C over the Tibetan Plateau, Japan, and marginal seas of northeastern Asia until the end of 21st century under SSP5-8.5. The spatial distribution of future winter DTR changes shows decreasing trends over northern China and Mongolia and increasing trends over southern China and Japan. Thus, it can be inferred that the population of Japan will be highly likely to suffer from shorter but colder winter with frequent cold surges in the future, while most of the population of China will experience a shorter and warmer winter. In addition, the winter precipitation intensity is projected to weaken in the precipitation band stretching from Taiwan to the region to the east of Japan, and there will be a drastic decline of surface snow amounts over the Tibetan Plateau.
The GHG forcing is revealed to play a crucial role in driving winter climate changes in East Asia. Mechanistic analysis suggests that GHG emission can cause significant atmospheric warming and facilitate a stronger and northward-positioned AL together with a weaker SH, EAT, and EAJS, resulting in a shorter and warmer winter in most of the East Asian continent. However, the intensification and poleward shift of the AL is expected to accelerate the low-level northerlies over the marginal seas of northeastern Asia, thus leading to future shorter and colder winter in these regions.

5. Discussion

This study revealed the winter climate variations across East Asia under different shared socioeconomic pathways, providing critical insights for evidence-based policy-making. By quantifying regional climate responses to varying emission pathways, the findings help policy-makers to achieve the following: (1) prioritize adaptation measures for projected temperature and precipitation anomalies [67]; (2) make emission reduction policies by demonstrating the specific regional benefits of climate mitigation; (3) optimize infrastructure investments in East Asia based on climate forecasts [68].
However, the results of this paper may have some limitations. First, the models spread in regional climate simulation, particularly for extreme events and precipitation processes, bringing uncertainty to our projections. Second, the coarse resolution of global models may inadequately capture characteristics of signals at small-to-medium scale, especially in complex terrain like the Tibetan Plateau. To address these two constraints, the downscaling method is required in future work focusing on regional climate variability.
In the objective eight-intraseasonal-monsoon-stage framework of [69,70], winter monsoon seasons in East Asia are divided into three stages consisting of early-winter, mid-winter, and late-winter, based on the 850 hPa horizontal winds. They find anthropogenic warming disrupts intraseasonal winter monsoon stages, implying more thorough analysis of winter climate changes is needed at the intraseasonal time scale. In addition, to determine the cause of the future precipitation changes, the moisture budget equation is used in previous studies [71,72], suggesting that the future enhancement of summer monsoon rainfall has various increasing rates depending on the monsoon’s sub-regions and it is mainly driven by changes in thermodynamic factors. However, there is a lack of understanding of the physical mechanisms underlying the future decrease in East Asian winter precipitation, which needs further investigation by means of diagnostic analysis and numerical simulations in future work.

Author Contributions

Conceptualization, Y.J. and W.L.; methodology, Y.J., Y.C., W.W. and J.S.; software, Y.J., Y.C. and W.W.; validation, W.L. and H.W.; formal analysis, Y.J., Y.C. and W.L.; investigation, Y.J., Y.C. and W.W.; resources, H.W. and J.S.; data curation, Y.C. and W.L.; writing—original draft preparation, Y.J.; writing—review and editing, W.L. and H.W.; visualization, W.L.; supervision, W.L. and H.W.; project administration, W.L. and H.W.; funding acquisition, W.L. and H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Key R&D Program of China, grant number 2022RDC2013304.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The CMIP6 data were acquired from the World Climate Research Programme (https://esgf-node.ipsl.upmc.fr/projects/cmip6-ipsl/, accessed on 5 September 2025). We are grateful to the European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/, accessed on 5 September 2025) and NCEP-NCAR (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html, accessed on 5 September 2025) for providing the reanalysis date.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area map with resident population density (unit: persons·km−2).
Figure 1. Study area map with resident population density (unit: persons·km−2).
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Figure 2. A schematic demonstrating the definition of the winter in a particular year on a grid point. A Savitzky–Golay filter is employed to the raw data of daily temperature (gray solid line) to remove the day-to-day fluctuations and obtain the annual cycle (red solid line). The horizontal light blue line indicates the 25th temperature threshold for winter. The vertical dark blue lines correspond to the onset and withdrawal of winter in this example, respectively.
Figure 2. A schematic demonstrating the definition of the winter in a particular year on a grid point. A Savitzky–Golay filter is employed to the raw data of daily temperature (gray solid line) to remove the day-to-day fluctuations and obtain the annual cycle (red solid line). The horizontal light blue line indicates the 25th temperature threshold for winter. The vertical dark blue lines correspond to the onset and withdrawal of winter in this example, respectively.
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Figure 3. Skill scores of SAT (red dots), SLP (blue dots), and 500 hPa geopotential height (green dots) in CMIP6 models relative to (a) ERA5 and (b) NCEP-NCAR Reanalysis-1 over East Asia (20–60° N, 70–150° E) in DJF during 1979–2014. The dashed lines are the corresponding mean skill scores of nine models.
Figure 3. Skill scores of SAT (red dots), SLP (blue dots), and 500 hPa geopotential height (green dots) in CMIP6 models relative to (a) ERA5 and (b) NCEP-NCAR Reanalysis-1 over East Asia (20–60° N, 70–150° E) in DJF during 1979–2014. The dashed lines are the corresponding mean skill scores of nine models.
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Figure 4. The climatological winter (a) onsets, (b) withdrawals, (c) lengths, and (d) SAT for the period 1979–2014.
Figure 4. The climatological winter (a) onsets, (b) withdrawals, (c) lengths, and (d) SAT for the period 1979–2014.
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Figure 5. The linear trends of winter onsets (unit: days·decade−1) for the period 1979–2014 under the (a) historical simulation and for the period 2015–2100 under the (b) SSP1-2.6, (c) SSP2-4.5, and (d) SSP5-8.5 scenarios. A positive (negative) value corresponds to a delay (an advance) in winter onset. Stippling indicates the 95% confidence level.
Figure 5. The linear trends of winter onsets (unit: days·decade−1) for the period 1979–2014 under the (a) historical simulation and for the period 2015–2100 under the (b) SSP1-2.6, (c) SSP2-4.5, and (d) SSP5-8.5 scenarios. A positive (negative) value corresponds to a delay (an advance) in winter onset. Stippling indicates the 95% confidence level.
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Figure 6. Same as Figure 5, but for winter lengths (unit: days·decade−1).
Figure 6. Same as Figure 5, but for winter lengths (unit: days·decade−1).
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Figure 7. Same as Figure 5, but for SAT within winters (unit: °C·decade−1).
Figure 7. Same as Figure 5, but for SAT within winters (unit: °C·decade−1).
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Figure 8. Temporal trends for winter (a) onsets, withdrawals, and (b) lengths during the periods of 1979–2014 (historical) and 2015–2100 (future). The black, blue, and red lines in the future zone denote the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The blue shading in (a) indicates the time range of traditional winter (DJF).
Figure 8. Temporal trends for winter (a) onsets, withdrawals, and (b) lengths during the periods of 1979–2014 (historical) and 2015–2100 (future). The black, blue, and red lines in the future zone denote the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The blue shading in (a) indicates the time range of traditional winter (DJF).
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Figure 9. The disappearance year of winter under the (a) SSP2-4.5 and (b) SSP5-8.5 scenarios. Here, the disappearance year of winter is defined as the first year when the winter does not appear over five consecutive years for the first time.
Figure 9. The disappearance year of winter under the (a) SSP2-4.5 and (b) SSP5-8.5 scenarios. Here, the disappearance year of winter is defined as the first year when the winter does not appear over five consecutive years for the first time.
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Figure 10. The linear trends of (a–c) daily maximum SAT, (d–f) daily minimum SAT, and (g–i) DTR (unit: °C·decade−1) within winters during the 2015–2100 period. The left, middle, and right panels represent the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Stippling indicates the 95% confidence level.
Figure 10. The linear trends of (a–c) daily maximum SAT, (d–f) daily minimum SAT, and (g–i) DTR (unit: °C·decade−1) within winters during the 2015–2100 period. The left, middle, and right panels represent the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Stippling indicates the 95% confidence level.
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Figure 11. Same as Figure 10, but for wintertime (a–c) precipitation (shading, unit: mm·day−1·decade−1) and (d–f) surface snow amounts (shading, unit: kg·m−2·decade−1), superposed with corresponding mean state fields (red contours) in winters of 2015–2100. The blue rectangles in (c,f) represent the areas used for Figure 12.
Figure 11. Same as Figure 10, but for wintertime (a–c) precipitation (shading, unit: mm·day−1·decade−1) and (d–f) surface snow amounts (shading, unit: kg·m−2·decade−1), superposed with corresponding mean state fields (red contours) in winters of 2015–2100. The blue rectangles in (c,f) represent the areas used for Figure 12.
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Figure 12. Averaged wintertime precipitation (blue solid line, unit: mm·day−1, left ordinate) and surface snow amounts (red solid line, unit: kg·m−2, right ordinate) within the boxes in Figure 11c and Figure 11f, respectively, under SSP5-8.5. The dashed lines are the corresponding linear trends.
Figure 12. Averaged wintertime precipitation (blue solid line, unit: mm·day−1, left ordinate) and surface snow amounts (red solid line, unit: kg·m−2, right ordinate) within the boxes in Figure 11c and Figure 11f, respectively, under SSP5-8.5. The dashed lines are the corresponding linear trends.
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Figure 13. The linear trends of (a1–a4) SAT (shading, unit: °C·decade−1), (b1–b4) SLP (shading, unit: hPa·decade−1), (c1–c4) 500 hPa geopotential height (shading, unit: gpm·decade−1), (d1–d4) 300 hPa zonal winds (shading, unit: m·s−1·decade−1), and the mean states of corresponding variables represented by the black contours in (b–d) in DJF. The four columns from left to right denote the historical simulations, SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Magenta contours are significant at the 95% confidence level. The blue rectangles in (b1,c1,d1) represent the area used for calculating the AL index, EAT intensity, and EAJS intensity, respectively.
Figure 13. The linear trends of (a1–a4) SAT (shading, unit: °C·decade−1), (b1–b4) SLP (shading, unit: hPa·decade−1), (c1–c4) 500 hPa geopotential height (shading, unit: gpm·decade−1), (d1–d4) 300 hPa zonal winds (shading, unit: m·s−1·decade−1), and the mean states of corresponding variables represented by the black contours in (b–d) in DJF. The four columns from left to right denote the historical simulations, SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Magenta contours are significant at the 95% confidence level. The blue rectangles in (b1,c1,d1) represent the area used for calculating the AL index, EAT intensity, and EAJS intensity, respectively.
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Figure 14. Temporal trends for the DJF-mean (a) SAT (unit: °C·decade−1) in East Asia, (b) AL index (unit: hPa·decade−1), (c) EAT intensity (unit: gpm·decade−1), and (d) EAJS intensity (unit: m·s−1·decade−1) during the period of 1979–2014. The upper (lower) horizontal line outside the box represents the maximum (minimum) value, the upper (lower) edge of the box is the 75th (25th) percentile, and the red dot (line) within the box denotes the mean (median).
Figure 14. Temporal trends for the DJF-mean (a) SAT (unit: °C·decade−1) in East Asia, (b) AL index (unit: hPa·decade−1), (c) EAT intensity (unit: gpm·decade−1), and (d) EAJS intensity (unit: m·s−1·decade−1) during the period of 1979–2014. The upper (lower) horizontal line outside the box represents the maximum (minimum) value, the upper (lower) edge of the box is the 75th (25th) percentile, and the red dot (line) within the box denotes the mean (median).
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Table 1. List of nine CMIP6 models used in this study.
Table 1. List of nine CMIP6 models used in this study.
Model IDInstitute IDCountryLongitude × Latitude
ACCESS-ESM1-5CSIROAustralia192 × 145
BCC-CSM2-MRBCCChina320 × 160
CanESM5CCCMACanada128 × 64
CESM2NCARUSA288 × 192
FGOALS-g3CASChina180 × 80
IPSL-CM6A-LRIPSLFrance144 × 143
MIROC6JAMSTEC, AORI, NIES, and R-CCSJapan256 × 128
MRI-ESM2-0MRIJapan320 × 160
NorESM2-LMNCCNorway144 × 96
Table 2. Correlation coefficients of yearly time series between the models and ERA5/NCEP-NCAR during 1979–2014.
Table 2. Correlation coefficients of yearly time series between the models and ERA5/NCEP-NCAR during 1979–2014.
Model NameSATSLP500 hPa Geopotential Height
ERA5NCEP-NCARERA5NCEP-NCARERA5NCEP-NCAR
ACCESS-ESM1-50.840.840.430.430.720.73
BCC-CSM2-MR0.840.850.430.420.730.74
CanESM50.850.850.400.390.750.76
CESM20.860.860.430.420.760.76
FGOALS-g30.850.850.400.400.740.75
IPSL-CM6A-LR0.850.850.390.380.730.73
MIROC60.840.840.440.440.710.72
MRI-ESM2-00.850.850.430.410.730.74
NorESM2-LM0.830.820.390.380.700.71
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Jiang, Y.; Chi, Y.; Wang, W.; Li, W.; Wang, H.; Sun, J. Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models. Atmosphere 2025, 16, 1143. https://doi.org/10.3390/atmos16101143

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Jiang Y, Chi Y, Wang W, Li W, Wang H, Sun J. Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models. Atmosphere. 2025; 16(10):1143. https://doi.org/10.3390/atmos16101143

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Jiang, Yuxi, Yutao Chi, Weidong Wang, Wenshan Li, Hui Wang, and Jianxiang Sun. 2025. "Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models" Atmosphere 16, no. 10: 1143. https://doi.org/10.3390/atmos16101143

APA Style

Jiang, Y., Chi, Y., Wang, W., Li, W., Wang, H., & Sun, J. (2025). Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models. Atmosphere, 16(10), 1143. https://doi.org/10.3390/atmos16101143

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