On 29 October 2024, Turís, Spain, recorded an unprecedented 24 h precipitation total of 771 mm, triggering one of the deadliest floods in Europe since 1900. Although recent attribution studies demonstrated that anthropogenic warming may have contributed to the event, the extent to which atmospheric circulation variability shaped its exceptional magnitude remains unclear. Using multi-source high-resolution precipitation observations, this study uncovers the dynamical and thermodynamical mechanisms driving the event. The exceptional precipitation was initiated by an anomalously deep upper-level cut-off low over southern Spain, accompanied by a southeasterly jet that enhanced the transport of warm, moisture-laden air from the Mediterranean. Circulation analog analysis reveals that pre-trough dynamics explain ∼31% (interquartile range: 7%–44%) of the maximum daily precipitation across eastern Spain during October–November 2024. Large-ensemble attribution using the HadGEM3-A-N216 model further confirms the crucial role of internal dynamics, which account for approximately one-third of the total anomaly, while the remaining signal is dominated by the combined contribution of anthropogenic forcing and internal thermodynamic processes. These results highlight the important role of atmospheric circulation in shaping Mediterranean precipitation extremes, emphasizing the need for explicitly accounting for circulation variability in event attribution and future regional flood risk assessment.

Compound dry-hot extremes exert stronger environmental impacts than individual dry or hot extremes. While evidence for increasing meteorological compound dry-hot extremes (defined using surface air temperature and vapor pressure deficit or precipitation) is growing, the impacts and evolving risks of soil-based compound dry-hot extremes remain poorly understood. Using homogenized soil temperature observations and observationally constrained soil moisture dataset for China, we show that the adverse effects of soil-based compound dry-hot extremes on vegetation productivity are more severe than their meteorological counterparts. From 1980 to 2017, the frequency and coverage area of soil-based compound dry-hot extremes in China increased by 3.0 days and 141.9 × 104 km2, respectively, with the most pronounced increases occurring in northern China. These increases are primarily attributed to anthropogenic soil warming. Under a fossil-fueled development scenario, the mean frequency of such extremes is projected to increase by 13.3 days by the end of the twenty-first century relative to the 1981-2010 baseline, potentially reducing China's terrestrial vegetation gross primary production by approximately 0.025 Pg C a-1. Our findings highlight an anthropogenic escalation of soil-based compound dry-hot extremes and their growing threats to terrestrial carbon sinks and food security.

Against the backdrop of global warming,China has been facing increasingly frequent and severe extreme weather and climate events,with a prominent risk of compound extreme events induced by interactions among multiple climate drivers and/or hazards.The present study first reviews the definition and classification of compound extreme events in China.Then,it summarizes research progress on the evolutionary characteristics,formation mechanisms,and future projections of different types of compound extreme events.The potential risks and pos-sible impact pathways of three specific event types—namely,continuous day-night hot extremes,temperature-humidity compound events,and high-temperature-ozone compound events—on the health of the Chinese pop-ulation are then explored.Finally,a framework for assessing the hazard risk of compound extreme events is constructed,accompanied by response strategies based on carbon neutrality targets.Building on existing re-search achievements,five future research directions are proposed:(1)identifying the risk chains of compound events;(2)addressing the constraints of observational records and coupled model performances;(3)attributing and understanding the drivers of compound extreme events;(4)finding optimal pathways for carbon reduction and air quality improvement;and(5)promoting inter-disciplinary,multi-regional,and cross-sectoral collabora-tion.Strengthening research in these directions will deepen our understanding of compound extreme events and provide technological support for climate change adaptation and health risk responses in China.

Recent wildfire outbreaks in Eastern Siberia herald far-reaching impacts on air quality and carbon balance. While anthropogenic warming is identified as the primary driver of increased wildfire risk, the role of internal climate variability remains unclear. Here, we perform an attribution analysis to reveal the impact of internal climate variability on the recent escalation of wildfires, using causal inference, path analysis, and large ensemble simulations. Our results show that the soaring wildfires in Eastern Siberia from 2001 to 2021 were mainly induced by a decline in fire-season precipitation of 16 mm decade-1, which accounts for approximately 42

The limited capabilities of global climate models in simulating mesoscale convective systems (MCSs) restrict our understanding of how global warming impacts MCSs. This study uses a high-resolution numerical model with large-ensemble experiments to simulate MCSs during the record-breaking extreme rainfall event in Henan Province, China, in July 2021. We compare the changes in the MCS's strength, size, and structure in a real-world simulation (RW) and a 0.8 degrees C colder simulation (analog to no-anthropogenic-warming-world simulation, short for NAWW) to assess the response of MCSs to global warming. Our results show that the total rainfall from the MCS increased by 10.0% in RW compared to NAWW, with a 3.1% increase in area and a 6.7% increase in rainfall intensity. The development of MCS becomes more rapid in response to warming since the pre-industrial era. The warmer and wetter climate results in higher convective available potential energy, and accelerates the MCS growth, but then the narrower low-convective inhibition regions suppress the continuous growth of MCS. During the mature phase, the maximum hourly rainfall intensity (Pmax) can increase by up to 26.5%/K, while Pmax locations can either remain unchanged or shift depending on the interaction of flows and terrains. These results highlight varying responses of MCSs to global warming during its different stages and provide valuable insights into the changing characteristics of extreme rainfall events under global warming. Mesoscale convective systems (MCSs) play a crucial role in generating heavy rainfall during severe rainfall events, such as the record-breaking rainfall occurred in Henan Province, China, in July 2021. Here, we investigate whether the MCSs during this extreme event are intensified due to global warming. We use a numerical model to simulate the MCSs under two different scenarios-the current climate background and a 0.8 degrees C colder climate, which analog to the pre-industrial climate. Our results indicate that the total rainfall from MCSs increased by 10.0% due to global warming, with a 3.1% larger MCSs area and a 6.7% stronger hourly rainfall intensity. Global warming also leads to a faster development of MCSs by providing favorable warmer and wetter atmosphere. When the MCSs are mature, the maximum hourly rainfall intensity can increase by up to 26.5% per degrees C. The center of MCSs could remain unmoved or shift, depending on the interaction between low-level flow and terrain. This study illustrates that MCSs can produce much stronger rainfall under global warming with varying changes during the developing and mature phases. These results provide deeper insight into the impacts of global warming to extreme rainfall events. Global warming intensifies the mesoscale convective system (MCS) during the record-breaking extreme rainfall event in July 2021 in Henan, China Global warming leads to a faster growth of MCS during its developing stage The interaction between terrains and moist flow may lead to different responses of rainfall centers to warming

Abstract Summer heatwaves are shifting from typical dry-heat to more humid conditions, with more frequent day-night compound heat extremes. As the fastest warming and aging continent, understanding the roles of climate and population dynamics is crucial for Europe to avoid underestimation of future heat-related health risks. Here we use a distributed lag nonlinear model that accounts for humid heat and day-night compound hot extremes to quantify future heat-related excess mortality in Europe due to changes in climate, population size, and age structure. We find humid heat can reduce the root mean square error of the model by 26 deaths compared to dry-bulb tempreature alone. Day-night compound hot extremes posing a 1.5 times higher risk of death than daytime only heat when lasting over 3 days. We project that total annual heat-induced mortality in Europe will increase by 24,670–69,827 deaths per degree of global warming under different Shared Socio-economic Pathways (SSPs), with western and northern Europe suffering the most. The increase in the future burden of heat-related mortality burden in Europe is dominanted by increased warming levels (accounting for 51.2–89.1% of the increase heat-related deaths) and an aging population structure (explaining 37.9–72.2% of the increase in the attributable heat-related mortality), although population decline under certain SSPs, such as SSP3 (a pathway of increased nationalism, regional rivalry, and decreased globalization), offsets over 34,000 heat-related deaths. Our analysis thus highlights the essential roles of mitigating and adapting to climate changes and increasing the resilience of older adults in reducing heat-related health risks in Europe.

Gridded meteorological datasets offer valuable data sources for monitoring and studying climate extremes. Based on the 2291 CMA meteorological observations, this paper evaluates and compares seven high-resolution gridded datasets and a global climate extremes indices datasets, HadEX3. Temporal and spatial consistency are assessed at the location of CMA observations. High-resolution gridded products and HadEX3 can effectively monitor the inter-annual changes in the overall mean of extreme temperature. The temporal consistency of precipitation indices is lower due to uneven spatial distribution of precipitation. Each high-resolution gridded dataset shows good consistency in depicting the spatial distribution patterns of extreme climate. However, at locations of the observations, different indices have larger or smaller biases. These are mainly due to the spatial scale of gridded datasets and calculation and operation order of extreme indices. The study emphasizes the importance of in-situ observations in improving data accuracy. At the same time, it is found that the generation method of the dataset has an impact on the quality, and satellite and model simulation data as background fields can compensate for the lack of spatial coverage of int-situ observations. In the study of extreme climate, appropriate datasets should be selected according to different research purposes.

Due to windward slope topography and monsoon activities, the populated Northeast Indian subcontinent (NEI) suffers from heavy rainfall and floods almost every year. Extreme persistent downpours lashed NEI in summer 2020, ranked the second heaviest on record since 1901. This event caused about 550 fatalities and economic loss up to hundreds of millions of dollars. It is highly compelling but challenging to understand the weather drivers and future risks of this high-impact event. Here, we suggested this event was likely caused by the anomalous anticyclone (AAC) over the Indo-Northwest Pacific region and La Niña-induced Walker circulation intensification. The overall effect of current human-induced climate change contributed little to the occurrence probability of this event, as most of the warming and wetting effects of greenhouse gases were canceled out by anthropogenic aerosols. Climate models project an increasing risk of 1.77 (1.97), 2.08 (2.59), 2.58 (3.88), and 3.10 (5.52) times of such extreme event in the median-term (long-term) future under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. It is mainly caused by the increases in atmospheric water vapor and 2020-like AAC frequency. Our findings indicate that future flooding risk over NEI will increase robustly if greenhouse warming continues.

As one of the largest arid and semiarid regions, Central Asia (CA) is prone to drought, which imposes significant impacts on human communities and ecosystems. Understanding the historical and future wetting/drying trend with the backdrop of climate change is paramount to sustainable development in CA. However, previous studies for the historical period yielded inconsistent results due to different data, study durations and methods used and those for the future period are rare. By analyzing the latest generated long-term (1894–2020) homogenized station observations, multiple global climate model (GCM) outputs and their dynamically and statistically downscaled results, we find robust historical and future drying trend in CA, especially in the growing season (April–September). Though there is an increasing tendency in regional precipitation during 1894–2020 in CA, the Standard Precipitation Evapotranspiration Index (SPEI) shows a decreasing trend due to the dominating influence of regional warming. Compared to the non-growing season (October–March), the decreasing trend of SPEI is more profound in the growing season. Moreover, the SPEI calculated based on the GCM outputs and their dynamically and statistically downscaled results consistently shows future drying trend in CA throughout the 21st century, which robustly holds against the approaches used to calculate potential evapotranspiration (i.e., Thornthwaite and Penman-Monteith equations). Besides SPEI, the simulated soil moisture of surface layer also exhibits a decreasing tendency. All these lines of evidence suggest robust historical and future drying trends in CA, which have important implications for climate change adaptation in this region.

Although the land-use harmonization (LUH) datasets have been widely applied in regional climate model (RCM) projections for investigating the role of the land-use forcing in future climate changes, few studies have thoroughly assessed them on local scale, which may bring large uncertainties in the resultant climate information for designing adaption and mitigation measures of climate change. The authors use a local land-use dataset (referred to as Li-LU) as the benchmark to assess the latest version of the LUH datasets, LUH2, in Central Asia (CA) which has undergone extensive land-use changes (LUCs) and might undergo extensive LUCs in the future. The results show that LUH2 has large biases in depicting the historical land-use states in CA for 1995–2015. For instance, the area of grassland (cropland) in LUH2 is about 1.4–1.5 (0.4–0.5) times of that of Li-LU. Moreover, the future LUCs predicted by LUH2 for 2045 (relative to 2005) are much smaller than those of Li-LU and these two datasets generally have opposite signals in changes. In addition, the predicted LUCs of LUH2 do not follow the causal mechanisms [the causal connections between the key drivers (e.g. population, economy, and environment) and land use] behind the LUCs in the past. If the future scenario of LUH2 is used for RCM projection in CA with the historical land-use information from Li-LU, the simulation results could be misleading for understanding the impacts of LUCs on future climate changes there. This study suggests that the LUH datasets should be carefully assessed before using them for regional studies and provides practical notes for selecting the appropriate land-use dataset for RCM projections in other areas around the world.

Apparent temperature (AP) and ground-level aerosol pollution (PM2.5) are important factors in human health, particularly in rapidly growing urban centers in the developing world. We quantify how changes in apparent temperature – that is, a combination of 2 m air temperature, relative humidity, surface wind speed, and PM2.5 concentrations – that depend on the same meteorological factors along with future industrial emission policy may impact people in the greater Beijing region. Four Earth system model (ESM) simulations of the modest greenhouse emissions RCP4.5 (Representative Concentration Pathway), the “business-as-usual” RCP8.5, and the stratospheric aerosol intervention G4 geoengineering scenarios are downscaled using both a 10 km resolution dynamic model (Weather Research and Forecasting, WRF) and a statistical approach (Inter-Sectoral Impact Model Intercomparison Project – ISIMIP). We use multiple linear regression models to simulate changes in PM2.5 and the contributions meteorological factors make in controlling seasonal AP and PM2.5. WRF produces warmer winters and cooler summers than ISIMIP both now and in the future. These differences mean that estimates of numbers of days with extreme apparent temperatures vary systematically with downscaling method, as well as between climate models and scenarios. Air temperature changes dominate differences in apparent temperatures between future scenarios even more than they do at present because the reductions in humidity expected under solar geoengineering are overwhelmed by rising vapor pressure due to rising temperatures and the lower wind speeds expected in the region in all future scenarios. Compared with the 2010s, the PM2.5 concentration is projected to decrease by 5.4 µg m−3 in the Beijing–Tianjin province under the G4 scenario during the 2060s from the WRF downscaling but decrease by 7.6 µg m−3 using ISIMIP. The relative risk of five diseases decreases by 1.1 %–6.7 % in G4, RCP4.5, and RCP8.5 using ISIMIP but has a smaller decrease (0.7 %–5.2 %) using WRF. Temperature and humidity differences between scenarios change the relative risk of disease from PM2.5 such that G4 results in 1 %–3 % higher health risks than RCP4.5. Urban centers see larger rises in extreme apparent temperatures than rural surroundings due to differences in land surface type, and since these are also the most densely populated, health impacts will be dominated by the larger rises in apparent temperatures in these urban areas.

Attribution of compound events informs preparedness for emerging hazards with disproportionate impacts. However, the task remains challenging because space-time interactions among extremes and uncertain dynamic changes are not satisfactorily addressed in the well-established attribution framework. For attributing the 2020 record-breaking spatially compounding flood-heat event in China, we conduct a storyline attribution analysis by designing simulation experiments via a weather forecast model, quantifying component-based attributable changes, and comparing with historical flow analogs. We quantify that given the large-scale circulation, anthropogenic influence to date has exacerbated the extreme Mei-yu rainfall in the mid-lower reaches of the Yangtze River during June–July 2020 by ~6.5% and warmed the co-occurring seasonal extreme heat in South China by ~1°C. Our projections show a further intensification of the compound event by the end of this century, with moderate emissions making the rainfall totals ~14% larger and the season ~2.1°C warmer in South China than the 2020 status.

Abstract Quantitative assessment of urbanization effect on surface air temperature (SAT) change provides crucial basis for formal detection and attribution analyses of climate change. However, debates about urbanization‐related warming bias in documented regional SAT trend still persist, mainly due to different determination of rural stations. Here the urbanization effect on SAT change over the Beijing–Tianjin–Hebei region in China during 1980–2019 is estimated through three kinds of ways (i.e., comparisons between urban and rural stations [arithmetically station‐averaged], urban‐dominated and rural‐dominated patches [patch‐weighted mean], and realistic urban and rural areas [area‐weighted mean]). The last method explicitly takes urban and rural land cover fractions into account when calculating urban/rural and regional mean SAT trends. Urbanization‐induced warming in the annual mean SAT change of urban stations (areas) through the three ways are estimated as 0.159°C, 0.195°C, and 0.138°C per decade, respectively. And urbanization effect on regional averaged annual mean SAT calculated by patch‐weighted and area‐weighted methods are 0.113°C and 0.050°C per decade, respectively, which account for 33.8% and 14.8% of the total regional warming. The urbanization effect on observed SAT change estimated by considering realistic urban/rural land cover proportions is much lower than traditional station‐unweighted way.

Anthropogenic forcing has approximately halved the probability of 2020 June-July persistent heavy mei-yu rainfall event based on HadGEM3-GA6 simulations without considering the COVID-induced aerosol emission reduction.

Unprecedentedly heavy rains hit China's Henan Province during 19-21 July 2021,triggering fatal and costly floods punctuated by over 120 billion Yuan of economic losses and nearly 400 fatalities.A broad swath of the province witnessed nearly a whole year's worth of rainfall pouring within the three days.The provincial cap-ital city Zhengzhou even registered 201.9 mm in an hour which smashed the records both locally and nationally.Such concen-trated,intense bursts of precipitation instantly overwhelmed the urban drainage system and sent torrents through streets and sub-way tunnels.Though rapid analyses sorted out favorable dynamic and thermodynamic setups,including exceptionally strong updrafts shaped by large-scale circulation anomalies,unique topography,and anomalously abundant water vapor advected jointly by the Northwestern Pacific subtropical high and double typhoons-In-Fa and Cempaka[1],the linkage between climate change and this record-shattering event also gained broad atten-tions vet remains elusive.

To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.

In this study, we investigate the climate attribution of the 21·7 Henan extreme precipitation event. A conditional storyline attribution method is used, based on simulations of the event with a small-domain high-resolution cloud-resolving model. Large-scale vertical motion is determined by an interactive representation of large-scale dynamics based on the quasi-geostrophic omega equation, with dynamical forcing terms taken from observation-based reanalysis data. It is found that warming may lead to significant intensification of both regional-scale (10–14% K −1 , depending on convective organization) and station-scale precipitation extremes (7–9% K −1 ). By comparing clustered convection organized by a localized surface temperature anomaly and squall-line convection organized by vertical wind shear, we further explored how convective organization may modify precipitation extremes and their responses to warming. It is found that shear convective organization is much more sensitive to large-scale dynamic forcing and results in much higher precipitation extremes at both regional and station scales than unorganized convection is. The clustered convection increases station-scale precipitation only slightly during heavy precipitation events. For regional-scale extreme precipitation sensitivity, shear-organized convection has a larger sensitivity by 2–3% K −1 than that of unorganized convection, over a wide temperature range, due to its stronger diabatic heating feedback. For the station-scale extreme precipitation sensitivity, no systemic dependence on convective organization is found in our simulations.

Central Asia (referred to as CA) is one of the climate change hot spots due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in this region. In this study, a high-resolution (9 km) climate projection dataset over CA (the HCPD-CA dataset) is derived from dynamically downscaled results based on multiple bias-corrected global climate models and contains four geostatic variables and 10 meteorological elements that are widely used to drive ecological and hydrological models. The reference and future periods are 1986–2005 and 2031–2050, respectively. The carbon emission scenario is Representative Concentration Pathway (RCP) 4.5. The evaluation shows that the data product has good quality in describing the climatology of all the elements in CA despite some systematic biases, which ensures the suitability of the dataset for future research. Main features of projected climate changes over CA in the near-term future are strong warming (annual mean temperature increasing by 1.62–2.02 ∘C) and a significant increase in downward shortwave and longwave flux at the surface, with minor changes in other elements (e.g., precipitation, relative humidity at 2 m, and wind speed at 10 m). The HCPD-CA dataset presented here serves as a scientific basis for assessing the potential impacts of projected climate changes over CA on many sectors, especially on ecological and hydrological systems. It has the DOI https://doi.org/10.11888/Meteoro.tpdc.271759 (Qiu, 2021).

Extreme persistent rainfall poses serious impacts on human and natural systems, predominately through its related hydrogeological disasters. Due to sustained heavy downpours, the summer of 2020 was the second wettest on record over Northeast Indian subcontinent since 1901. Here, we find that this orographically anchored extreme rainfall event was largely associated with the anomalous anticyclone (AAC) over the Indo‐Northwest Pacific region and La Niña‐induced Walker circulation intensification. The overall effect of anthropogenic forcings contributed little to the occurrence probability of this event, because the warming and wetting effects of greenhouse gases were almost negated by anthropogenic aerosols. Climate models project a prominent increasing trend of such extreme event under future greenhouse‐induced warming due to increase in atmospheric water vapor and 2020‐like AAC frequency. Our findings thus call for scaling up climate change adaptation efforts for increasingly extreme persistent rainfall in highly populated but low‐resilience South Asian developing countries.