Marine heatwaves (MHWs) are prolonged anomalies of warm sea surface temperature (SST) that can disrupt marine ecosystems, physical climate processes, and human coastal activities. MHW definitions vary due to different stakeholders requirements, such as ecological scientists and climate scientists having differing yet specific thresholds and metrics. Here we introduce a new global dataset of daily MHW metrics: climatological baselines, threshold exceedances, SST anomalies, and categorical event classifications of severity, derived from the European Space Agency SST Climate Change Initiative (ESA SST CCI) climate data record (CDR; 1982–2021) version 3.0 and an extension from 2022–2024 provided as an interim climate data record (iCDR). Building on the widely used definition of MHWs, periods in which SST exceeds the local 90th percentile for 5 or more days, our dataset extends this framework by incorporating multiple baseline climatologies (including fixed 30-year periods and rolling 30-year windows, as well as the period for reanalysis 1993–2016), varied percentile thresholds (90th, 95th, 99th), and both raw and linearly detrended SST anomalies. We also implement alternative event duration criteria (minimum 10-day and 30-day persistence) to classify longer-lasting warm events. All data products are provided at daily resolution on a 0.05° (~5 km) grid, with outputs including daily climatological percentiles, SST anomalies and binary MHW flags with severity category indices. This comprehensive dataset provides a consistent foundation for detecting and analysing MHWs across time and space, enabling researchers to assess how methodological choices affect MHW characterisation. By offering multiple definitions in parallel, the dataset facilitates intercomparison studies and supports applications from climate monitoring and model evaluation to marine ecological impact assessment, thereby providing users with pre-made indices for extremes.

As climate change prospects point towards the pressing need for local-scale adaptation measures, it becomes of utmost importance to recognise intra-urban hotspots of excessive heat exposure. Accordingly, there is a pressing societal need to downscale the available near-surface air temperature predictions (reanalysis, forecasts, or projections) from the regional (kilometric) to a local (sub-kilometric) scale, to facilitate the identification of short-term critical areas within metropolitan regions of cities, where acclimatisation and public health preventive measures actions should be prioritised. This work presents a Machine Learning (ML)-based methodology for upsampling near-surface air temperature from a 2.5x2.5Km to a 125×223m (0.002x0.002°) grid, the Urban Thermal Signal Downscaling (UTS-D) model. The model was developed over four Danish Functional Urban Areas - Copenhagen, Aarhus, Aalborg and Odense - using crowdsourced data from citizen-own weather stations, along with geospatial predictors that are well-known determinants of the Urban Heat Island (UHI) effect. The best overall results were achieved with a Random Forest model with an overall Mean Absolute Error (MAE) of 0.76K for the overall test subset, 0.66K during HW and 0.85K during CW subsets, denoting the model’s good performance under extreme heat conditions. When evaluating against reference in-situ stations, the model’s accuracy shows a slight decrease compared to the background numerical weather predictor model, suggesting that, by training solely on crowdsourced stations (clustered in urban areas), there is a tendency towards overestimating rural air temperatures. Nevertheless, regarding the spatial pattern, the model captures the thermal signature of the underlying urban infrastructure, allowing to disclose neighbourhood-level details.

Intra-urban air temperature is highly heterogeneous, and coarse-resolution products cannot resolve the neighbourhood-scale variability that urban heat management requires. The objective of this study is to assess whether the Urban Thermal Signal Downscaling (UTS-D) methodology, previously developed and tested over Danish Functional Urban Areas (FUAs) with the DAnish ReAnalysis (DANRA), can be re-implemented with a pan-European reanalysis and retrained in cities with contrasting climates, and to quantify the incremental value of a vegetation-seasonality predictor. The approach is applied to four European FUAs spanning contrasting climate zones, i.e., Copenhagen (Denmark), Lisbon (Portugal), Seville (Spain) and Athens (Greece). Two modifications were introduced: the regional weather component was replaced with the pan-European Copernicus Regional Reanalysis (CERRA), enabling application beyond Denmark, and a monthly climatological Normalized Difference Vegetation Index (NDVI) was added as a non-fixed predictor to capture the seasonality of vegetation cooling. A Random Forest model was trained to predict the residual bias between CERRA 2 m air temperature and quality-controlled observations from citizen-owned weather stations, combined with static landscape and urban predictors. Using a 2019–2022 training/cross-validation strategy and an independent 2023 test year, the downscaled model substantially outperformed raw CERRA in all four cities, reducing the test-year mean absolute error from 1.02–1.99 °C to 0.69–1.06 °C (a 32–55% reduction) and reaching R² of 0.94–0.98. Absolute error reductions were particularly relevant on hot days (HD) (0.52–1.21 °C); however, such periods are also the ones during which CERRA errors tend to be largest, indicating that the hot-day added value mainly reflects the correction of a larger reanalysis bias. NDVI provided only a marginal additional improvement (≤0.02 °C; ≤2%), statistically significant but negligible in practical terms. Errors were lowest in Copenhagen, which has the densest citizen-station network, and highest in Seville, which has the sparsest; however, a network-thinning experiment showed that station density explains only part of these differences, pointing to the combined role of station density, the magnitude of the reanalysis error, climate and urban morphology. These results show that UTS-D is methodologically transferable, i.e., it can be retrained successfully with CERRA wherever sufficiently dense citizen-station observations exist, and provide a basis for future heat-health and urban adaptation applications.

Marine heatwaves (MHWs) are prolonged periods of anomalously warm ocean temperatures, usually detected through Sea Surface Temperature (SST). Deeply connected to climate change, these extreme events signal how global warming affects the Earth’s subsystems, driving an uptick in the corresponding impacts. Key aspects related to MHWs are (i) the understanding of the MHWs drivers, (ii) the establishment of standard and (ii) the estimation of expected impacts. Through a multi-project journey, +ATLANTIC has been investigating these topics, to contribute to filling the existing knowledge gaps and to promote the societal uptake of satellite-based MHWs geospatial information.Regarding (i) in the scope of the Horizon Europe ObsSea4Clim project, which aims to support the GCOS with novel indicators, we have been investigating the relationship between MHWs and the large-scale climate modes that modulate the North Atlantic basin climate. In addition to SST, several ocean variables, including mixed-layer depth and ocean currents, are combined with atmospheric drivers such as air-sea heat fluxes, wind speed, and sea level pressure to describe the most severe MHWs events, recognizing how pressure systems. Preliminary results indicated the importance of stable high-pressure systems in driving air-sea heat flux anomalies, particularly net, latent and sensible heat exchanges, which lead to lower wind speeds and increased energy absorption by the ocean, modulating the intensity and persistence of MHWs over the mid-latitude regions; specifically, the Portuguese EEZ.Regarding (ii) we propose to evolve from a pixel-wise detection to an events-based labelling algorithm that allows to distinguish noisy pixel patches from meso-scale impactful events. Within the framework of XHEAT, an ESA-funded project, we are exploring the usage of ML/AI in post-processing the traditional MHWs methods to establish a novel mechanism that allows to study of specific events as a continuous element. Furthermore, it allows us to better investigate how large-scale MHWs over the North Atlantic can lead to certain continental extremes over Europe, particularly Compound Drought-Heatwaves (CDHW). Results already suggest that by adopting such techniques, more meaningful relationship patterns emerge, paving the way for probabilistic seasonal outlook predictions.Regarding (iii) within the ESA-funded CAREHeat, the Impact Use Cases have allowed working alongside aquaculture producers to evaluate the MHW impacts in their activities. From the conducted work, several findings can be highlighted: (1) from the consulted users, all show an interest and willingness to adopt climate data on their operations, especially where local-specific indicators may impact their decision-making on short or long-term activities; nonetheless, few are capable to using geospatial products routinely; (2) the dialogue with the Early Adopters must be kept regularly, to ensure that the objective formulation of the use cases problem is scientifically sound but resonates with their needs; and (iii) biological data available is too limited to establish statistically significant relationships that span decadal time series, which hinders our capacity to measure impacts effectively, which indicates that further effort should be employed in biological data acquisition.

Marine Heatwaves (MHWs), defined as prolonged periods of anomalously warm ocean temperatures, have gained increasing scientific attention due to their significant ecological and socioeconomic consequences. Despite advances in MHW detection and characterisation, the mechanisms driving their onset and evolution remain an active area of research. As part of the Horizon Europe ObsSea4Clim project, this study investigates the influence of atmospheric circulation and climate modes of variability on MHWs in the North Atlantic basin. Preliminary findings underscore the critical role of large-scale atmospheric circulation, particularly the positioning and intensity of high-pressure systems, in modulating air–sea heat flux anomalies. These circulation patterns suppress wind speeds, enhance oceanic heat absorption, and consequently influence the spatial distribution, intensity, and duration of MHWs. To better capture the spatiotemporal coherence of these events, we advance from a pixel-wise to an event-based detection framework, enabling the labelling and ranking of spatially organised, scale-dependent MHWs. Enhancements to the spatial filtering and labelling algorithm further improve the detection of event structure and propagation pathways. Building on previous analyses, we apply non-linear statistical techniques, including Spearman’s rank correlation and Mutual Information, to more robustly quantify relationships between MHW characteristics and atmospheric drivers. Composite analyses for positive and negative phases of the North Atlantic Oscillation (NAO) are refined using a monthly NAO index and complemented with the Eastern Atlantic (EA) index, allowing for a more detailed representation of temporal variability. Furthermore, the statistical significance of these results is tested. Together, these advances deepen understanding of the atmospheric drivers of North Atlantic MHWs and enhance the potential for improved prediction of extreme ocean temperature events.

Extreme heat is increasingly recognized as one of the most severe climate-related risks affecting urban populations, with disproportionate impacts on public health, energy systems, and vulnerable communities. As heatwaves intensify under climate change, cities require near-real time, high-resolution and actionable information to support early warning systems, preparedness, and long-term adaptation. Addressing this challenge at urban-local scale demands not only methodological innovation, but also robust digital infrastructures capable of delivering consistent and interoperable climate intelligence across regions.Destination Earth (DestinE), a strategic initiative of the European Union, represents a transformative step in this direction by providing global, high-resolution climate and weather simulations, through Digital Twins of the Earth System. By coupling advanced numerical models, Earth Observation (EO) data, and high-performance computing, DestinE establishes a common backbone for next-generation climate services. However, translating these powerful datasets into locally relevant, operational products for cities remains a critical challenge.DE_395-Urban Heat Health Forecasting (UHHF) project addresses this gap by demonstrating how DestinE Extremes Digital-Twin outputs can be transformed into urban-scale user-oriented heat-health indicators through the operational use of Machine Learning (ML). The project applies ML-based downscaling techniques to near-surface air temperature (T2m) and relative humidity (RH) forecasts, enhancing spatial resolution from kilometre-scale to approximately 200 m. These downscaled fields are subsequently used to derive human-biometeorological indicators such as the Universal Thermal Climate Index (UTCI) and Thermal Stress Duration (TSD), supporting health-oriented risk assessment.The UHHF framework integrates DestinE atmospheric drivers with EO-derived and geospatial predictors describing urban form, land cover, vegetation, and topography, including Local Climate Zones. Quality-controlled crowdsourced observations from citizen weather stations are combined with WMO reference data to constrain and validate the ML models, ensuring robustness under both average and extreme conditions. The approach is being implemented across four climatically and socio-environmentally diverse Functional Urban Areas, e.g. Naples, Chicago, Santiago, and Cape Town, enabling a systematic evaluation of models across continents.By building directly on DestinE and complementary European programmes led by ECMWF, ESA, and Copernicus, drawing on both their data assets and operational services, UHHF aims to illustrate how these can be leveraged to develop affordable, scalable, and reproducible urban-scale climate information and services. The project highlights the strategic importance of climate data platforms in bridging the gap between global simulations and local decision-making, contributing to the development of interoperable urban climate and health services aligned with European and international resilience frameworks.