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Antarctica directly impacts the lives of more than half of the world’s population living in the coastal regions. Therefore it is highly desirable to project its climate for the future. But it is a region where the climate models have large inter-modal variability and hence it raises questions about the robustness of the projections available. Therefore, we have examined 87 global models from three modelling consortia (Coupled Model Intercomparison Project Phase 5 (CMIP5), Coupled Model Intercomparison Project Phase 6 (CMIP6), and NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)), characterized their fidelity, selected a set of ten models (MM10) performing satisfactorily, and used them to make the future projection of precipitation and temperature, and assessed the contribution of precipitation towards sea-levels. For the historical period, the multi-model mean (MMM) of CMIP5 performed slightly better than CMIP6 and was worse for NEX-GDDP, with negligible surface temperature bias of approximately 0.5 °C and a 17.5% and 19% biases in the mean precipitation noted in both CMIP consortia. These biases considerably reduced in MM10, with 21st century projections showing surface warming of approximately 5.1 °C–5.3 °C and precipitation increase approximately 44%–50% against ERA-5 under high-emission scenarios in both CMIP consortia. This projected precipitation increase is much less than that projected using MMM in previous studies with almost the same level of warming, implying approximately 40.0 mm yr −1 contribution of precipitation towards sea-level mitigation against approximately 65.0 mm yr −1 .

This paper evaluates the NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP) CMIP6 models’ performance in simulating extreme climate indices across China and its eight subregions for the period 2081–2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The models effectively reproduce the spatial patterns of extreme high temperatures, especially in northern China. They show enhanced capabilities in accurately simulating the maximum daily maximum temperature (TXx) and the number of high temperature days (T35). They improve the cold bias of the TXx index in Northwest China and warm bias in South China. In terms of precipitation, the models demonstrate strong performance, evidenced by significant spatial correlations in total wet day precipitation (PTOT) simulations. They reduce the biases of PTOT and simple daily intensity (SDII) compared to CMIP6 models. Regionally, they enhance PTOT accuracy along southern coasts and in Yunnan, better captures very heavy precipitation days (R20) in the Southwest region, max 5-day precipitation (RX5D) in North China and Southwest region, and SDII in the Northeast region and Yunnan. Under SSP5-8.5 scenario, significant impacts include increased TXx in Northwest China, more heatwave days in Southwest China, and more T35 in South China. Extreme precipitation will become more frequent in South and East China, with the greatest intensity increases in Southwest China (SWC1). North China will see fewest consecutive dry days (CDD) indices, while consecutive wet days (CWD) will prominently rise in SWC1.

The increase in record‐breaking extreme events caused by climate change poses a threat to human health and well‐being. Understanding the future impacts of such events on global populations can provide decision‐making support for policies aiming to mitigate climate change. Here, we investigated the population exposure to eight climate extreme indices and drivers of exposure trajectories based on National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections Coupled Model Intercomparison Project 6 and population projection data under four shared socioeconomic pathway scenarios at a spatial resolution of 0.25° × 0.25°. The results show that by the mid‐twenty‐first century, most regions worldwide, especially Africa and South America, will continue to experience record‐breaking temperatures and compound drought and heatwaves (CDHWs). Regarding population exposure, under SSP3‐7.0 in the late twenty‐first century, the mean value of the multimodel median expected annual exposure (EAE) of all extreme temperature indices and CDHW reaches 8.12 billion persons per year. Population exposure hotspots will be concentrated in Central Africa, South Asia, Southeast Asia, and East Asia, mostly in developing countries, where 55.01%–87.42% of the EAE is found. The drivers of exposure trajectories are spatially heterogeneous. The increase in record‐breaking probability contributes more than population growth to EAE growth in most regions of the world except Central Asia, the Middle East, and most of Africa. These findings reveal the future trajectories of record‐breaking probabilities and population exposures for climate extremes, which can inform understanding of the intersections between climate change and population change and future risk management. Plain Language Summary Climate change causes rapid increases in extreme weather events that threaten human health and well‐being. Understanding the future impacts of climate change on the global population can inform policies aiming to mitigate climate change. Here, we investigated the spatiotemporal dynamics of future record‐breaking extreme temperature and precipitation events, sequential precipitation and heatwaves (hot extremes after heavy precipitation), and compound drought and heatwaves (cooccurring dry and hot extremes) (CDHWs) and analyzed how populations may be potentially affected by these events based on the latest available climate model data and future population projections. The results show that by the mid‐twenty‐first century, most regions worldwide, especially Africa and South America, will continue to experience record‐breaking temperatures and CDHWs. Regions where populations will be most affected include Central Africa, South Asia, Southeast Asia, and East Asia, mostly developing countries. This increase in the affected population is due to the growth of the population and the increase in record‐breaking extreme events; record‐breaking extreme event increases contribute more than population growth in most regions of the world except Central Asia, the Middle East, and most of Africa. Overall, this study projected the spatiotemporal patterns of unprecedented climate extremes and affected populations to inform risk management. Key Points Africa and South America will experience successive record‐breaking extreme events and even compound droughts and heatwaves Population exposure will be highly uneven and largely concentrated in underdeveloped areas A record‐breaking increase in probability will be the major driver of the increase in population exposure in most regions of the world

The Paris Agreement and the Special Report on Global Warming of 1.5 °C from the Intergovernmental Panel on Climate Change (IPCC) highlighted the potential risks of climate change across different global warming levels (GWLs). The increasing occurrence of extreme high-temperature events is linked to a warmer climate that is particularly prevalent in the Arabian Peninsula (AP). This study investigates future changes in temperatures and related extremes over AP, under four GWLs, such as 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C, with three different Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study uses high-resolution datasets of 27 models from the NASA Earth Exchange Global Daily Downscaled Projections of the Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6). The results showed that the NEX-GDDP-CMIP6 individual models and their multi-model means reasonably captured the extreme temperature events. The summer maximum and winter minimum temperatures are projected to increase by 0.11–0.67 °C and 0.09–0.70 °C per decade under the selected SSPs. Likewise, the projected temperature extremes exhibit significant warming with varying degrees across the GWLs under the selected SSPs. The warm temperature extremes are projected to increase, while the cold extremes are projected to decrease under all GWLs and the selected SSPs. Overall, the findings provide a comprehensive assessment of temperature changes over AP in response to global warming, which can be helpful in the development of climate adaptation and mitigation strategies.

最近,NASA发布了一套基于CMIP5 21个耦合模式输出的高分辨率降尺度逐日数据集,简称NEX-GDDP。本文评估了NEX-GDDP对中国极端降水的模拟性能。研究发现:(1)相比CMIP5直接输出结果,NEX-GDDP能够更好刻画中国极端降水的空间分布;(2)未来中国极端降水事件明显增多、强度增强,NEX-GDDP在区域尺度上给出了更多的气候变化信息;(3)NEXGDDP预估的中国未来极端降水变化的不确定性范围相比CMIP5直接输出结果明显减少,使得预估结果更加可靠.

We identified a set of bias-corrected and downscaled Coupled Model Intercomparison Project 6 (CMIP6) models capable of accurately simulating the observed mean Indian summer monsoon rainfall, extreme rain events (EREs) and their respective interannual variability. The future changes in EREs projected by these models for four climate change scenarios—Shared Socioeconomic Pathways (SSPs), 1–2.6, 2–4.5, 3–7.0 and 5–8.5 were estimated using percentile thresholds. Under the highest emission scenario, SSP5-8.5, at the end of the century, summer monsoon season total rainfall exhibits a 1.1-fold increase, while extreme rainfall intensity demonstrates a more substantial rise of 1.3-fold. The spatial variability of seasonal total rainfall increases by 1.2-fold compared to the baseline period, with an even more pronounced 2.1-fold increase in the spatial variability of extreme rainfall (R99p). These findings underscore the significant amplification of rainfall variability and intensity under the most extreme climate scenario. The intensity and frequency of very extreme rainfall events (vEREs) were also found to increase, though with a substantial inter-model spread. Under SSP5-8.5, extreme rainfall intensity scales with temperature at 1.5 to 2 times the Clausius-Clapeyron (CC) rate. While mid-century scenarios show minimal variations in extreme rainfall intensity from the historical period, end-century projections reveal significant shifts; an increase in north India and a decrease in south India due to cloud-induced cooling effects.

Global climate models (GCMs) are the state-of-the-art tool for understanding climate change and predicting future. However, little research has been reported on the latest NEX-GDDP-CMIP6 product in China. The purpose of this study was to evaluate the simulated performance and drought capture utility of the NEX-GDDP-CMIP6 over China. First, the simulation skills of the 16 GCMs in NEX-GDDP-CMIP6 was evaluated by the 'DISO', a big data evaluation method. Second, the DISO framework for drought identification was constructed by coupling the Correlation Coefficient (CC), False Alarm Rate (FAR) and Probability of Detection (POD). Then, it was combined with SPI and SPEI to evaluate the drought detection capability of NEX-GDPD-CMIP6. The result shows that: (1) NEX-GDPD-CMIP6 can reproduce the spatial distribution pattern of historical precipitation and temperature, which performs well in simulating warming trend but fails to capture precipitation's fluctuation characteristics. (2) The best performing model in precipitation is ACCESS-CM2 (DISO 1.630) and in temperature is CESM2 (DISO 3.246). (3) The 16MME performs better than the best single model, indicating that multi-model ensemble can effectively reduce the uncertainty inherent in models. (4) The SPEI calculated by 16MME identifying drought well in arid, while SPI is recommended for other climate classifications of China.

A new bias-corrected, statistically downscaled product, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, has been developed and released to help in understanding climate change at local to regional scales. Here, we evaluate the performance of the NEX-GDDP data in simulating daily maximum temperature (TX) and daily minimum temperature (TN) in the historical period 1961–2005 over China at national and regional scales. Projected future changes in TX and TN are assessed under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emissions scenarios. Results show that the NEX-GDDP data can capture the basic spatial patterns of TX and TN, but these results underestimate the warming trends of TX and TN from 1961 to 2005 over China. The largest biases are found in western China due to its complex terrain conditions; these biases are 2.33 and 2.21 times larger than those found in eastern China for TX and TN, respectively. The climate projections show that the difference in uncertainties is small between the east and the west, and higher warming changes correspond to greater uncertainties. The increasing trends under the RCP8.5 are 2.22 and 2.31 times the size found under the RCP4.5 by the end of the twenty-first century for TX and TN, respectively. The Tibetan plateau has the fastest warming trend under the two scenarios.

Using a downscaled high‐resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) dataset based on Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, this study evaluated and compared extreme hot days (EHDs) over the three mega‐regions [the Eastern United States (EUS), Europe (EU) and Eastern Asia (EA)] during the historical period (1981–2005) against observations, resulting in a subset of models with high skill for the past climatology and trend. The observed EHDs over EU exhibit the largest absolute amount and the most significant increases in frequency (4.0 days·decade−1), intensity (0.22°C·decade−1) and extent (8.4°C·days·ecade−1), while no significant trend over EUS is found. Compared with the observation, the largest bias in NEX‐GDDP is the remarkably overestimated increase in the trend over EUS. In the RCP8.5 projection using six models with relatively high fidelity, the increase of EHDs is evidently enhanced during 2030–2054 over the three regions, particularly EUS. The projected trend of EHDs over EUS could be undetermined because of the modelling biases in aerosol effects and internal variation, which is worthy of further investigation in CMIP6. (a) We evaluated the historical fidelities and projections of extreme hot days (EHDs) over the three global mega‐regions [the Eastern United States (EUS), Europe (EU) and Eastern Asia (EA)] in high‐resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) dataset. (b) The observed EHDs over EU exhibit the most significant increases, while no significant trend over EUS is found. (c) The largest uncertainty of the future projection trend ratio (projection/historical) occurs over EUS because of large warming biases in historical simulations.

High Mountain Asia (HMA), which includes the Tibetan Plateau, Tienshan Mountains and surrounding region, has abundant snowfall and a long period of snow cover annually. The headwaters of many prominent Asian rivers depend in part on HMA meltwater. In this study, we evaluate projected changes in mean snowfall (Smean), snowfall days (Sd), and snowfall fraction (Sf) for the years 2070-2099 relative to 1976-2005, under the Representative Concentration Pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) emission scenarios. An evaluation of the results shows that while NASA's NEX-GDDP (National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections) high-resolution daily downscaled dataset can successfully capture the distribution of mean snowfall climatology, it has a strong bias for extreme snowfall indices. In general, the projected increase of temperature under RCP4.5 and RCP8.5-especially in winter-will result in a decrease in snowfall amount (−18.9%, −32.8%), fewer snowfall days (−29.6%, −47.3%), and less precipitation falling as snow (−26.7%, −42.3%). Furthermore, under high emission scenarios, rain-dominated regions are projected to expand 53.9%, while snow-dominated areas will only account for 17.9% of the entire HMA. Spatially, snowfall shows a more robust decline in eastern HMA (e.g. East Tienshan, East Kun Lun, Qilian, South and Eastern Tibet, and Hengduan) than in western HMA (e.g. Hissar Alay, Pamir, and Karakoram). This difference can be attributed to various environmental factors, such as climatology, elevation influences, and the unique seasonal recycle between the two regions.