The Nile Basin has been the focus of research on the links between climate variability, extreme events and social events over the last 2000 years of history. Unfortunately, we currently only have global climate and Earth system model data with low spatial resolution for this period. To understand how climate affects society, we must rely on proxy data, as global climate models lack the necessary regional process detail. Our goal is to enhance our understanding of the historical climate of the Nile Basin on a regional scale using the customized COSMO-CLM paleoclimate model. We intend to run simulations with COSMO-CLM (forced by MPI-ESM-LR) by using the “past2k” simulation set up with an optimized configuration for both the present (1979–2019 CE) and paleo periods (500–2000 BCE) at resolutions of around 50 and 12 km. In this approach, we tried to systematically consider orbital, solar, and volcanic influences, as well as changes in vegetation, land use, and greenhouse gas concentrations in the simulation. We found that the temperature simulations are overall in agreement with the reanalysis data and the observational record. However, it is worth noting that our precipitation simulations performed relatively weakly, especially in the Nile Basin.

Heatwaves are among the most impactful climate extremes in Europe, driving acute health risks and socio-economic disruption. They are a challenge for early warning and public understanding due to uncertainties in event onset, severity, and human response. Building on the interdisciplinary Strengthening the Research Capacities for Extreme Weather Events in Romania (SCEWERO) project funded by the European Union, this study investigates how scientific evidence, perception data, and communication strategies interact within Romania’s heatwave Early Warning System operated by Meteo-Romania. We analyse both empirical perception data — collected through structured surveys and focus groups to quantify how different communities interpret heat warnings, risk levels, and confidence intervals — and observational heatwave metrics to map divergences between communicated risk and public understanding. This research highlights specific sources of uncertainty faced by forecasters (e.g., variable heat exposure, model forecast spreads), and documents how these uncertainties are interpreted or misinterpreted by non-expert audiences. By tracing how uncertainty in forecast signals propagates through institutional warning messages and into public perception, we identify communication gaps that can lead to maladaptive responses or reduced trust in warnings during heat events. Framing uncertainty, contextualised risk information, and tailored communication strategies improve both public comprehension and behavioural intent during heatwave alerts. We propose evidence-based recommendations for operational Early Warning Systems that move beyond fixed deterministic thresholds, instead incorporating probabilistic messaging where appropriate and grounding risk communication in locally derived perception data. This work illustrates how harmonising scientific uncertainty communication with Early Warning practices can strengthen societal resilience to heatwaves, offering a transferable framework for climate risk communication in other European regions.

Teleconnections play a fundamental role in shaping global climate variability and the occurrence of extreme events. The El Niño–Southern Oscillation (ENSO) is one of the most influential large-scale modes, with well-documented impacts on the global climate system. Although ENSO exerts only a modest influence on European seasonal climate, previous studies suggest that a link emerges during late winter. This period is of particular relevance for agriculture, as anomalously warm conditions can trigger early crop development and thereby increase vulnerability to subsequent cold extremes such as spring frosts. As warm winters are projected to become more frequent under future climate change, understanding the large-scale drivers of these conditions is increasingly important for mitigating socio-economic impacts on agriculture. While the general relationship between ENSO and European late-winter climate has been widely studied, the specific role of ENSO in triggering anomalous warm conditions that initiate early-season agricultural risk has not yet been systematically assessed. Establishing this statistical linkage will provide valuable insights for impact assessment and could improve the predictability of climate-related risks.To assess teleconnection interactions, dimension-reduction techniques such as Empirical Orthogonal Functions and Canonical Correlation Analysis (CCA) are among the most widely used approaches. However, these methods are inherently linear and typically restricted to interactions between two spatial fields, which limits their ability to capture complex nonlinear dependencies. Here, we introduce a novel dimension-reduction framework designed to identify nonlinear interactions among multiple climate variables. The approach integrates kernel generalized CCA with multiple kernel learning and preimages, enabling the extraction of spatially interpretable coupled climate patterns that can serve as a basis for defining teleconnections. By employing an automatic kernel-selection procedure, the framework captures both linear and nonlinear dependencies among the analysed climate variables. We apply this methodology to assess the influence of ENSO on European temperature and water balance anomalies and benchmark the results against a purely linear formulation using the Twentieth Century Reanalysis, version 3 (20CRv3), over the period 1900–2015.Our results show that the nonlinear framework identifies a substantially larger fraction of Europe being influenced by ENSO than is suggested by linear approaches. The ENSO signal exhibits a pronounced asymmetry across the distributions of temperature and water balance anomalies, with lower and upper extremes responding in different ways. In particular, the upper percentiles of temperature, representing warm and hot extremes over most of Europe, including central Europe, show a clear ENSO-related signal associated with La Niña events that are preceded by El Niño Modoki conditions. The Central European region is of high relevance for agricultural production, suggesting that non-linear ENSO effects may play an important role in shaping early-season climate risk. On the other hand, water balance anomalies primarily respond in the central part of the distribution and are mainly linked to El Niño events. Both signals are significantly weaker or absent in classical linear analyses. Overall, these findings highlight the added value of nonlinear methods for revealing previously hidden teleconnection impacts and point to the benefits for improving climate risk assessment and seasonal prediction.

Abstract. This study presents the evaluation of the historical reference simulations (1961–1990) of the NUKLEUS ensemble, the first kilometer-scale convection-permitting multi-model climate ensemble for Germany. The main goal is to examine to what extent these high-resolution simulations can provide high-quality and actionable information for climate adaptation measures in Germany. The NUKLEUS ensemble comprises nine members, generated by dynamically downscaling three global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) with three regional climate models to a 3 km grid over a Central European domain. The evaluation focuses on the spatio-temporal and statistical representation of basic meteorological variables (temperature, wind speed, and precipitation) and derived application-relevant climate indices compared to reanalyses and observation-based data sets. The analyses are performed for Germany and six pilot regions representing diverse climatic and physiographic settings. The results reveal that the ensemble overall exhibits moderate biases with temperature and precipitation generally showing high distributional skill. Only few simulations exhibit strong warm biases, particularly during summer, while all simulations exhibit a wet bias throughout the year, except local dry biases during summer in individual members. Wind biases are more heterogeneous, particularly due to reference data constraints over complex terrain. Percentile-based climate indices are well reproduced, while fixed-threshold indices show systematic deviations. A comparison with previous regional climate model ensembles highlights the added value of the here-chosen multi-model approach. With regard to downstream applications, a quantile (delta) mapping bias correction is applied to daily precipitation totals and daily mean, minimum, and maximum temperature, which removes climatological biases and markedly improves threshold-based indices. The paper also demonstrates important limitations of this bias correction approach for event-based applications, showing that it may disrupt spatial coherence and introduce spurious spatial gradients in precipitation fields, which can affect the characterization of extreme precipitation events. Overall, the presented analyses support the use of the NUKLEUS ensemble as a high-resolution basis for regional climate and impact studies, while underlining that application-oriented post-processing and validation should be tailored to the target variable and use case.

Anticipating climate-related crop stress requires crop-risk information that is spatially explicit, probabilistic and fast enough for large seasonal forecast and climate-scenario ensembles. We present the Surrogate Engine for Crop Simulations Framework (SECSF), a deep-learning framework that emulates the process-based ECroPS model using only daily minimum and maximum temperature and precipitation. Trained on ERA5-forced ECroPS simulations for grain maize and spring barley, SECSF closely reproduces daily crop-growth dynamics and harvest timing while reducing computational cost by around four orders of magnitude, enabling ensemble-scale inference suitable for research and operational pipelines such as agricultural early warning and adaptation planning under uncertainty across seasonal-to-climate timescales. When forced with seasonal forecast data, SECSF captures spatially coherent crop-risk patterns across Europe in the high-impact year 2022 and is consistent with independent monitoring, supporting its use for probabilistic Areas of Concern. Under CMIP6 scenarios, SECSF identifies the Mediterranean basin as a hotspot of maize-risk signals through mid-century, with more mixed signals in central and northern Europe.

Spatially synchronous extreme warm events can amplify environmental and societal impacts by affecting multiple regions simultaneously. However, previous studies have largely focused on June–August (JJA) events during the instrumental period, limiting understanding of their seasonal prevalence and long-term evolution. Here, we examine globally synchronous seasonal warm events (GSSWEs) using seasonal mean near-surface air temperature (SAT) and introduce an Extremity Index (EI) that combines normalized local land SAT anomaly intensity with the spatial extent of land areas exceeding a specified SAT threshold. Using instrumental observations, paleo-reanalysis products and climate model simulations, we assess changes in global-land GSSWE extremity since 850 Common Era (CE). We find that GSSWEs have intensified sharply since the 1970s across all four seasons, reaching levels not found earlier in the multi-dataset records. Detection and attribution analyses indicate that anthropogenic forcing, dominated by greenhouse-gas forcing, is the main contributor to this recent intensification. Spatially, the tropics contribute greatly to the recent extremity of GSSWEs due to their lower intrinsic variability. These findings highlight that climate-risk assessment and adaptation planning need to consider the growing spatial coherence and intensity of seasonal warm extremes. This study shows that the extremity of globally synchronized seasonal warm events has sharply intensified in recent decades, reaching unprecedented levels since 850 CE, largely driven by anthropogenic greenhouse gas forcing.

Heatwaves (HWs) are among the most damaging climate extremes affecting the Mediterranean basin, where they could drive excess mortality, agricultural losses, water stress and wildfire risk. The Mediterranean region is warming fast, and the frequency, duration and intensity of HWs are projected to keep rising. Anticipating these events requires understanding their drivers. The complex interaction between large-scale atmospheric circulation, remote teleconnections and local land-surface conditions is difficult to capture with conventional statistical or dynamical approaches, and these drivers may vary markedly from one Mediterranean sub-region to another.This work proposes the application of a general driver identification framework, Spatio-Temporal Cluster-Optimized Feature Selection (STCO-FS), to identify the key short-term and seasonal drivers of HWs across the Mediterranean basin. The method combines clustering algorithms for reducing the spatial dimensionality with an ensemble evolutionary optimization algorithm to perform driver selection jointly in the spatial and temporal domains. In a first phase, gridded predictor fields from the ERA5 reanalysis, such as mean sea level pressure, geopotential height at 500 hPa, sea surface temperature, soil moisture, total precipitation and 2 m temperature, are reduced in dimensionality by grouping grid points with similar temporal behaviour into clusters. Climate variability indices (e.g. NAO, ENSO, IOD) and local variables are added directly. In a second phase, a wrapper feature selection approach based on a multi-method evolutionary algorithm (PCRO-SL) selects the most skilful drivers and identifies, for each one, the optimal time lag and time window, distinguishing short-term precursors (days) from sub-seasonal and seasonal influences (up to several months) of HW occurrence.The framework will be evaluated on representative areas of the Mediterranean. We expect that this approach will allow us to unravel the relative contribution of the different variables, and to characterise how these contributions differ across sub-regions of the basin. By revealing the spatio-temporal structure of HW drivers, this framework aims to improve the physical understanding and sub-seasonal predictability of Mediterranean HWs, supporting more effective early warning and climate adaptation strategies.

The Greater Ordos Region (GOR), located at the interface between the East Asian Summer Monsoon and mid-latitude westerly circulation systems, is highly sensitive to both oceanic forcing and regional land–atmosphere interactions. This study synthesises annually resolved tree-ring and documentary records with lower-resolution evidence from lake sediments, aeolian archives, and pollen data to reconstruct hydroclimatic and temperature variability over the past ~3500 years. The multi-proxy evidence reveals pronounced alternations between wetter and drier conditions across successive dynastic periods. High-resolution records resolve the timing, duration, and severity of extreme events, including multi-decadal droughts during the late Han and Tang periods and a widespread megadrought in the early seventeenth century CE associated with crop failures and societal stress. Lower-resolution archives provide longer-term context, documenting progressive shifts towards increased aridity, steppe expansion, and desertification, particularly following major drought episodes. The combined proxy approach demonstrates how recurrent hydroclimatic extremes, interspersed with phases of recovery, have exerted a persistent influence on agricultural systems, land-use dynamics, and societal stability. Integrating high- and low-resolution climate records allows assessment of both abrupt climate shocks and longer-term environmental trends that have shaped regional vulnerability through time.

Investigating long-term archaeological land-use patterns is crucial for understanding how past populations adjusted settlement strategies, resource exploitation, and communication networks in response to environmental and climatic variability. Recent quantitative archaeological research has provided important insights into long-term spatio-temporal social transformations in the Carpathian Basin. Yet the diachronic relationships between communication networks and environmental and climatic changes remain poorly understood. To fill this gap, we present a standardized, transparent, and replicable framework to compare relative connectivity across distinct site-distribution patterns, using a center-periphery model derived from custom network analyses spanning the Neolithic to the Hungarian Conquest period (similar to 8000-1000 BP). We develop and present climatically adjusted and simulated movement networks based on spatially clustered centers, emphasising high site intensities and travel cost efficiency derived from a weighted walking friction model incorporating topography and hydrology. We further compare land-use patterns and socio-ecological persistence with crop cultivation strategies based on carbon and nitrogen stable isotope data from human and animal remains. Our results show period-specific differences in centre-periphery structures and accessibility inequalities, alongside changing associations between archaeological footprints and descriptive environmental classes. These patterns are consistent with repeated reorganisation under changing environmental conditions, rather than with a uniform response to environmental change.

Heat waves (HWs) are complex, multivariate, extreme weather events that cause significant harm to human health, ecosystems, and economies. Correct detection and attribution of HWs to anthropogenic climate change is important to better understand the underlying mechanisms and to improve predictions. In this work, we address this issue and propose a multivariate version of a hybrid approach to reconstruct heat waves, consisting of the AM and deep Autoencoders (MvEA-AM algorithm), improving existing less effective methods used until now, such as the multivariate Analogue Method (MvAM). The proposed hybrid approach produces a more reliable representation of the event than the classical MvAM for reconstructing and attributing HWs in Europe. The explainable and interpretable analysis of the obtained results is based on leveraging the SHapley Additive exPlanations (SHAP) method to explain deep learning algorithms, a capability that is not achievable with the MvAM. This explainability analysis shows that our model learns useful features during the training of the algorithm, which are aligned with the Physics of the problem, and employs the correct features during reconstruction and attribution analysis of the HWs considered.

Sociocultural niche construction is a key mechanism behind the current planetary crisis. Its causal drivers in past societies remain poorly understood due to a lack of data and modeling complexities. Here, we apply a Bayesian machine learning algorithm to a geospatial quantitative dataset compiled from 15th-16th c. CE Ottoman taxation registers, to explore shifts in drivers of land use choices in village communities in southern Greece. Greek and Albanian communities living within the same landscape experienced a transition from unstable political conditions and limited commodification of agriculture to lasting peace, intense market integration and state fiscal pressure, characteristic for early European capitalism. We found that prior to this economic transformation, ethno-cultural identities (Greek or Albanian) played an important role in local ecological niche construction. However, within two human generations (50 years), village communities shifted away from relying on ethnic identities in their ecological choices and their strategies became strongly connected to the investment potential of each village community and the environmental characteristics of their surroundings. Thus, our analysis demonstrates that the pressures of early modern socio-economic systems necessitated highly adaptive behavior from local communities that reduced the ecological significance of traditional identities.

We present the Surrogate Engine for Crop Simulations for Maize (SECS4M), a deep-learning emulator designed to replicate the process-based ECroPS crop growth model for grain maize in Europe while enabling computationally efficient, large-scale applications in climate services. SECS4M is built on a nested Long Short-Term Memory architecture capturing short- and long-term weather–crop interactions, while it ingests only three daily meteorological inputs, minimum and maximum temperature and total precipitation, thus minimizing the uncertainty that follows the use of a much wider input stream as in ECroPS. Trained on ERA5-forced yield outputs, SECS4M accurately reproduces crop growth trajectories, harvest timing, and yield distributions. Computational requirements are reduced from ~70s to ~0.008s per grid-cell–year, a four-order-of-magnitude speed-up that enables ensemble-scale, operational use.Forced with bias-adjusted SEAS5.1 forecasts, SECS4M reproduces observed 2022 impacts and supports probabilistic identification of Areas of Concern (AoC) based on tercile-based yield anomalies. Under CMIP6 scenarios SSP3-7.0 and SSP5-8.5 to 2050, the emulator highlights specific regions as persistent hotspots of yield risk, while others exhibit mixed signals. SECS4M thus provides a scalable, digital twins enabled and data-efficient framework for seasonal forecasting, AoC mapping, and scenario analysis. Finally, the methodology can be extended to other crops and can be tested for its potential on other regions.

Abstract. The past 2500 years were marked by major historical developments across the eastern Mediterranean, the Middle East, the Arabian Peninsula, and the Nile Basin from Lake Victoria to the Nile Delta. Modeling efforts by both the global and regional climate modeling communities remain limited in this region. Here, we address this gap by presenting the first transient regional climate simulation for the area spanning 2350 years, from 500 BCE to 1850 CE, using the COSMO-CLM model. The simulation reveals an exceptionally pronounced climatic response to the consecutive volcanic eruptions of 536 and 540 CE, which motivated an additional century-long ensemble experiment to investigate this interval in greater detail. The eruptions produce marked surface cooling through reduced incoming solar radiation and are accompanied by large-scale circulation anomalies. In the simulation, widespread cooling persists until around 550 CE, with boreal summers showing the strongest anomalies during the first two to three years after the eruptions. Precipitation responses display strong regional contrasts: anomalously wet conditions occur over the Mediterranean, the Middle East, and Southeast Africa, particularly during the climatologically dry Northern Hemisphere summer season, whereas the Sahara, the Arabian Peninsula, Central Africa, and Northeast Africa experience concurrent dryness concentrated in their respective rainy seasons. The most severe climatic anomalies occur within the first one to two years after the eruptions and gradually weaken over the following years.

Data related directly to the First Plague Pandemic [FPP] (541–750 CE) in the Byzantine Empire has been gathering for over a century. Initially, only textual evidence was used, resulting in varied interpretations. In recent decades, however, a wider range of materials from natural sciences and archaeology has been included in FPP reconstructions. The methods used to analyze and interpret these data differ across disciplines and have varying levels of margin of error. The cross-disciplinary use of such data, still in early development, has produced interpretations that are sometimes debated, mainly due to a lack of familiarity and ongoing communication among relevant fields. Even within the same dataset, like written sources, the persistent use of significantly different approaches has resulted in various FPP reconstructions. Our goal is to change that by reexamining both the original data from the eastern and central Mediterranean from the mid-sixth to the mid-eighth century and the methods behind them. We explore new data and apply methods from a broad range of disciplines to identify gaps and uncertainties and suggest ways to address them.

Heatwaves have been widely studied in recent years because of their major impact on human health, mortality, ecosystems, agriculture, and the economy. Globally, heatwaves are becoming more severe, longer, and recurrent with global temperature rise. Therefore, the study of heat waves and the development of an early warning system for prediction of regional heatwaves help climate preparedness and decision-making. In this research, we propose a heatwave prediction algorithm based on a deep learning model, a convolutional neural network (CNN). This CNN model is trained with reanalysis data ERA5 and real heatwave events from EMO observation data for years from 1993 to 2021. We illustrate the relationship between the patterns in geopotential height at 500 hpa (GPH), sea surface temperature (SST), and the real heatwaves that happened in the last 20 years. This study employs the hindcast data from SEAS5.1 with 25 ensemble members, available at C3S. GPH and SST from observation data are input to the model and the heatwave magnitude at every single grid point is the output. The heatwave is defined as a period of three or more consecutive hot days and nights when the daily maximum and minimum temperature (TX/TN) exceeds the long‐term (1993–2022) daily 90th percentile. For estimating the heat wave magnitude we accumulated TX exceedance the local 90th percentile for all heat wave days over a user-defined interval (monthly, seasonal, etc.) as in Zampieri et al. (2017), Toreti et al. (2019). The results show the CNN model using atmospheric circulation fields (SST and GPH) with adjusted parameters is able to forecast extreme events in Europe, and it can potentially enhance the AI-based early warning systems for extreme weather.Zampieri, M., Ceglar, A., Dentener, F., and Toreti, A. (2017). Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environmental Research Letters, 12 (6), 064008. doi:10.1088/1748-9326/aa723bToreti, A., Cronie, O., and Zampieri, M. (2019). Concurrent climate extremes in the key wheat producing regions of the world. Scientific Reports, 9(1), 5493. doi:10.1038/s41598-019-41932-5

Many studies have shown that compounding extreme events are likely to exacerbate socio-economic risks compared to single extremes. Despite this important fact, studies focussing on the connectivity of extreme events and their associated impacts frequently have some shortcomings. First, extreme events such as droughts and heat waves are often predefined through thresholds, restricting the class of meteorological events leading to the observed impacts. The choice of threshold for defining these extreme events is also often of meteorological and/or statistical nature and thus potentially unsuitable for the holistic identification of the associated impacts. Furthermore, impacts can arise from combinations of non-extreme events that might fall short of the threshold-based identification, thereby limiting the ability to account for key dynamics that determine the risk associated with compound events. Our study aims to overcome those shortcomings by linking climate events with their observed impacts in agriculture. We analyse wet and warm late winters followed by dry and hot springs, and the associated agricultural damages in Europe with the aim of reconstructing these compound events based on the observed impact. A first analysis is conducted for winter wheat impacts in France, the largest European winter wheat producer. We identify agro-climatic zones based on multivariate time series clustering and employ a regularized generalized canonical correlation analysis to identify the large-scale drivers of crop variability for these regions. The patterns that emerge from the analysis are characterized by wet and warm conditions in January and February linked to a positive North Atlantic Oscillation (NAO) state, followed by warm and dry conditions in April induced by a tripole with a blocking high over Central Europe. Using imbalanced random forests, we construct objective bounds and define thresholds to identify which temperatures are warm enough or which water balances are low enough to be associated with significant effect on crop yield reduction. Our results indicate that imbalanced random forests can predict these types of events reasonably well at the local scale, and that the derived thresholds are mostly lower than the commonly used thresholds for detecting similar extreme events. The latter illustrates that the combination of non-extreme climate events can indeed be detrimental to agricultural production in Europe, which is also crucial as the analysed types of events are predicted to occur more often in the future as a result of climate change.

Background: The German registry of aortic dissections type A (GERAADA) is a large European registry documenting patients with type A aortic dissection who have undergone surgical repair. This analysis investigated a potential association between the incidence of type A dissections (AADA) and lunar cycles, day of the week, or weather conditions. Methods: Data from 2,388 patients were analysed for two endpoints: incidence of AADA surgery per day (analysed using a Poisson regression to account for overdispersed data) and early mortality after surgery (analysed using a logistic mixed regression to account for centre heterogeneity). In both models, the influence of weather conditions (season, temperature, temperature difference, radiation, and synoptic conditions), moon phase, and weekday of operation were examined. Results: The occurrence of AADA surgery was similar between weekdays (Monday to Friday), but less frequent on weekends. The 30-day mortality odds ratio was higher for surgeries performed on weekends than on weekdays. Operations were more frequent in winter than in other seasons. The occurrence of surgery or early mortality after surgery was not associated with synoptic weather conditions. Mean daily temperature and global radiation were not found to be different between survivors and patients with early deaths. No significant association was found between the moon phase and the occurrence or the outcome of surgery. Conclusion: The occurrence of AADA surgery was higher during winter, with colder mean temperatures, and lower on weekends than on weekdays. Mortality after surgery on weekends was higher than after surgery on weekdays.

Drought and heat events in Europe are becoming increasingly frequent due to human-induced climate change, impacting both human well-being and ecosystem functioning. The intensity and effects of these events vary across the continent, making it crucial for decision-makers to understand spatial variability in drought impacts. Data on drought-related damage are currently dispersed across scientific publications, government reports, and media outlets. This study consolidates data on drought and heat damage in European forests from 2018 to 2022, using Europe-wide datasets including those related to crown defoliation, insect damage, burnt forest areas, and tree cover loss. The data, covering 16 European countries, were analysed across four regions, northern, central, Alpine, and southern, and compared with a reference period from 2010 to 2014. Findings reveal that forests in all zones experienced reduced vitality due to drought and elevated temperatures, with varying severity. Central Europe showed the highest vulnerability, impacting both coniferous and deciduous trees. The southern zone, while affected by tree cover loss, demonstrated greater resilience, likely due to historical drought exposure. The northern zone is experiencing emerging impacts less severely, possibly due to site-adapted boreal species, while the Alpine zone showed minimal impact, suggesting a protective effect of altitude. Key trends include (1) significant tree cover loss in the northern, central, and southern zones; (2) high damage levels despite 2021 being an average year, indicating lasting effects from previous years; (3) notable challenges in the central zone and in Sweden due to bark beetle infestations; and (4) no increase in wildfire severity in southern Europe despite ongoing challenges. Based on this assessment, we conclude that (i) European forests are highly vulnerable to drought and heat, with even resilient ecosystems at risk of severe damage; (ii) tailored strategies are essential to mitigate climate change impacts on European forests, incorporating regional differences in forest damage and resilience; and (iii) effective management requires harmonised data collection and enhanced monitoring to address future challenges comprehensively.

Background:The German Registry of Aortic Dissections Type A (GERAADA) is a large European registry documenting patients with type A aortic dissection who have undergone surgical repair. This analysis investigated a potential association between the incidence of type A dissections (AADA) and lunar cycles, day of the week, or weather conditions. Methods:Data from 2,388 patients were analyzed for two endpoints: incidence of AADA surgery per day (analyzed using a Poisson regression to account for overdispersed data) and early mortality after surgery (analyzed using a logistic mixed regression to account for center heterogeneity). In both models, the influence of weather conditions (season, temperature, temperature difference, radiation, and synoptic conditions), moon phase, and weekday of operation was examined. Results:The occurrence of AADA surgery was similar between weekdays (Monday to Friday), but less frequent on weekends. The 30-day mortality odds ratio was higher for surgeries performed on weekends than on weekdays. Operations were more frequent in winter than in other seasons. The occurrence of surgery or early mortality after surgery was not associated with synoptic weather conditions. Mean daily temperature and global radiation were not found to be different between survivors and patients with early deaths. No significant association was found between the moon phase and the occurrence or the outcome of surgery. Conclusion:The occurrence of AADA surgery was higher during winter, with colder mean temperatures, and lower on weekends than on weekdays. Mortality after surgery on weekends was higher than after surgery on weekdays.