Marine renewable energies can be considered as the world’s largest untapped renewable energy resource. The demand for forecasting power generation at sub-seasonal to seasonal (S2S) timescales has been growing to aid in managing the production and storage of the offshore renewable energies. At these timescales, atmospheric circulation is predominantly governed by recurrent weather regimes (WRs). In this study, we investigated the impact of winter WRs on both wind and wave potential energies along the Ibero-Moroccan Atlantic region. An optimal number of 7 WRs based on 500-hPa geopotential height winter anomalies was selected via the Weighted Information Criterion, these regimes were specifically defined to distinguish their respective impacts on wind and wave power generation. Results show that the positive phase of the North Atlantic Oscillation is associated with the maximum of the wave and wind power, especially over the Iberian coasts and Bay of Biscay (up to + 100

Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or empirical statistical techniques. GCMs are widely used but require substantial computational resources, limiting their capacity. In contrast, statistical methods often lack robustness due to the short historical records available. Recent works propose machine learning methods trained on climate model output, leveraging larger sample sizes. Yet, many of these studies focus on prediction tasks that may be restricted in spatial or temporal extent, thereby creating a gap with existing operational predictions. Others fail to disentangle the sources of skill in the context of climate change, where strong trends provide spurious estimates. This study combines variational inference with transformers to predict global and regional seasonal anomalies of temperature and rainfall. The model is trained on output from CMIP6 and tested using ERA5 reanalysis data. Temperature predictions demonstrate skill beyond the climatology and climate-change trend and even outperform the numerical state-of-the-art system SEAS5 in some ocean and land areas. Precipitation forecasts show more limited skill, with fewer regions outperforming climatology and fewer surpassing SEAS5. Furthermore, the consistency found in both teleconnections and skill spatial patterns against SEAS5 suggests that both systems build on similar sources of predictability.

Aedes -borne diseases, such as dengue, Zika, and chikungunya, pose a significant threat to millions of people worldwide each year. Considering the relationships between the emergence of these diseases and anthropogenic climate change, it has become imperative for health authorities to maintain detailed surveillance of key environmental variables that can trigger epidemic episodes. While disease transmission is generally conditioned by multiple socio-economic factors, environmental suitability for vectors and viruses to proliferate is a necessary—although not sufficient—condition that needs to be closely monitored. To this end, the Aedes-borne dIsease monitoring of environmental suitability (AIMES) system improves on the disease transmissibility framework of AeDES, the most up-to-date system of this kind in the current literature. AIMES broadens both the temporal and spatial scope of its predecessor, while simultaneously enhancing the quality of the environmental suitability index. It provides continuously updated monthly historical values derived from multiple observational references, enabling a robust quantification of the observational uncertainty.

Abstract Subseasonal temperature forecasts can guide timely action against heat risks, but their coarse resolution limits regional usefulness. We apply a subseasonal downscaling framework to benchmark 27 statistical methods, including configurations of bias correction, linear and logistic regression, and analogs, to assess how they transfer forecast skill from coarse to local resolution at the weekly scale. As a test case, retrospective forecasts from CFSv2 (Climate Forecast System version 2; ~100 km) are downscaled to ~ 5 km for initializations issued one to four weeks before each target week of the Paris 2024 Olympics. Skill is assessed with the Brier Skill Score at the 10th and 90th percentiles for extremes. We also test whether incorporating atmospheric patterns adds value to downscaling. Methods are implemented with both daily and weekly data to examine the role of temporal resolution. Results show that, although most methods successfully transfer CFSv2 skill to higher resolution, method choice remains critical, as some degrade skill while others enhance it. Methods incorporating atmospheric patterns show promise at longer lead times when the relevant pattern, climatologically a key driver of heat in the target week, is well predicted. At the subseasonal scale, downscaling with weekly predictors outperforms that based on daily predictors.

During the last decades, climate services have been exponentially proliferating in number and diversity. However, in many cases, a profound understanding, coordination, and synergies among the different climate services are missing. This limits society’s opportunities to increase resilience to climate impacts, or—what is worse—the number and diversity of disconnected climate services available to stakeholders can delay, freeze, or even dismantle ongoing adaptation strategies and action plans. Building healthy ecosystems of climate services is a way to guarantee that society enhances resilience, while optimally orchestrating the available resources. A healthy ecosystem implies having the capacity to adapt to changes in the demand of the climate services, their risks, and values. These ecosystems tend to be more robust to climate impacts than an uncoordinated collection of climate services focused on certain applications or just one sector because the shocks to one part of the ecosystem are redistributed. After introducing the concept of climate services ecosystem (CSE) and describing its elements, this paper shows the dynamic relationship between CSE, its value, risk, and resilience. The climate services ecosystem approach assesses the value of an integrated collection of climate services, under scenarios of a changing climate, changes of societal needs, and limited budgets, supporting stakeholders to define what, when, and how to fund climate services.

While subseasonal forecasts often exhibit limited skill across mid-latitudes, occasional improvements are observed in specific locations during certain periods, known as "windows of opportunity." Understanding the causal factors behind these windows is complex due to the diverse and interdependent nature of predictors, their spatial and temporal variability, and the challenges in establishing causality relationships. Traditional lagged-correlations methods provide only a partial view, lacking insights into causality. Based on previous work on the role of land surface processes, multi-model subseasonal model skill assessment and the use of causality metrics in predictions across timescales (e.g. Ardilouze et al., 2020, 2021; Materia et al 2020, 2022; Muñoz et al., 2023), here we propose an approach based on the Liang-Kleeman information flow, allowing the assessment of statistically significant causal links across various lead times.Applied to reforecast and reanalysis data, our framework successfully identifies significant predictability drivers -involving sea-surface temperatures, atmospheric circulation and remote and local land-surface processes-, revealing their interference (interplay), evolving patterns and prevalence from seasonal to subseasonal scales. Furthermore, the comparison between reanalysis and reforecast results aids in assessing the capability of models to capture these causality features, suggesting additional ways to conduct model diagnostics. We illustrate here the theoretical background by showcasing the causal factors influencing a window of opportunity identified from a multimodel subseasonal reforecast. ReferencesArdilouze, C., Materia, S., Batté, L., Benassi, M., & Prodhomme, C. (2020). Precipitation response to extreme soil moisture conditions over the Mediterranean. Climate Dynamics, 1, 1–16. https://doi.org/10.1007/S00382-020-05519-5/TABLES/2Ardilouze, C., Specq, D., Batté, L., & Cassou, C. (2021). Flow dependence of wintertime subseasonal prediction skill over Europe. Weather and Climate Dynamics, 2(4), 1033-1049. https://doi.org/10.5194/wcd-2-1033-2021 Materia, S., Muñoz, Á. G., Álvarez-Castro, M. C., Mason, S. J., Vitart, F., & Gualdi, S. (2020). Multi-model subseasonal forecasts of spring cold spells: potential value for the hazelnut agribusiness. Weather and Forecasting. https://doi.org/10.1175/waf-d-19-0086.1 Materia, S., Ardilouze, C., Prodhomme, C., & et al. (2022). Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime. Climate Dynamics, 58, 1943–1963. https://doi.org/10.1007/s00382-021-05815-8Muñoz, Á. G., Doblas-Reyes, F., DiSera, L., Donat, M., González-Reviriego, N., Soret, A., Terrado, M., & Torralba, V. (2023). Hunting for “Windows of Opportunity” in Forecasts Across Timescales? Cross it. EGUGA, EGU-15594. https://doi.org/10.5194/EGUSPHERE-EGU23-15594

In a global warming scenario, there is a growing need to understand why the Euro-Mediterranean region is a hotspot for both warming and drying signals and the seasonal and regional details, thus providing improved climate information required by decision makers. This work focuses on the design of an evaluation framework for climate simulations based on a classification of synoptic circulation patterns (CPs). A set of 30 CMIP6 global climate models (GCMs) is evaluated in terms of how well they reproduce the spatio-temporal variability of a CPs classification within the region. CPs are constructed through a hierarchical clustering procedure, using daily mean sea level pressure (SLP) during 1950-2014 against the ERA5 reanalysis. The link with surface variables -including precipitation, minimum and maximum temperatures- is also studied. Model performance is quantified based on different metrics for the spatial and temporal representation of the SLP patterns and the associated surface conditions, allowing a ranking of the best-performing GCMs. GCMs adequately reproduce the annual cycle of the CPs frequency, with a dominant synoptic structure during summertime enhancing warm and dry conditions. Best-performing models in this regard include MPI-ESM1-2-LR, EC-Earth3-CC and MRI-ESM2-0. However, the correct timing of this CP and the transitional patterns are often misrepresented, such as in GFDL-ESM4 and NorESM2-LM. The analysis of the surface patterns associated with each CP show good model skills, better for the extreme temperatures than for rainfall and particularly during the transition seasons, for which the GCMs spread also increases. In this sense, the models EC-Earth3-Veg, EC-Earth3-CC and GFDL-CM4 present the best scores, whereas INM-CM5-0, KIOT-ESM and NorESM2-LM show the lower skills. By blending both the spatial and temporal features of the CPs, the EC-Earth3-CC, EC-Earth3-Veg, GFDL-CM4 and MRI-ESM2-0 arise as the best-performing GCMs over the Euro-Mediterranean region. Overall, it is highlighted that not all models perform best in all the aspects considered, emphasizing the need of a complete process-based model evaluation. This is a way to constrain the future projections in order to reduce uncertainty and come up with coherent climate information at a regional scale.

The performance of a set of 26 CMIP6 global climate models (GCMs) in the Euro-Mediterranean region is analyzed based on a classification of atmospheric circulation patterns (CPs). Their spatial and temporal variability representation, including the associated surface conditions in ERA5 during 1950-2014, allows a ranking of the best-performing GCMs. GCMs manage to reproduce the annual cycle of the CPs frequency, with a dominant summer CP enhancing warm and dry conditions. However, the correct timing of this pattern and the transitional CPs often need to be more accurate. The analysis of the surface patterns related to the different CPs presents overall good model performance, higher for temperatures than for precipitation, particularly in the transition seasons, for which the GCMs spread in their skill score increases. By blending both the spatial and temporal features of the CPs, the EC-Earth3-CC, IPSL-CM6A-LR, EC-Earth3-Veg-LR, MIROC6, and GFDL-ESM4 arise as the best-performing GCMs. This ranking is used to construct multiple model ensembles of climate projections, also taking into account model dependence and spread. Results from this assessment show that future projections of extreme climate indices (2070-2 100)-including the expected increases in the frequency of warm days and dry spells-can be "performance-constrained" and their uncertainty can be more reliably assessed by selecting specific subsets of GCMs, generating tailored climate information at a regional scale. In particular, the warming and drying signals are clearer in the best-performing GCMs, with more robust results in summer than in winter.

ABSTRACTDevelopment and dissemination of seasonal forecasts are integral components of the climate services provided by numerous meteorological services worldwide, offering estimates of meteorological variables on a seasonal time scale to aid local warning systems and decision‐making processes. The World Meteorological Organization (WMO) recommends that operational seasonal forecasts be objective and that the process be traceable and reproducible, including the selection and calibration of models. Following these guidelines, the Chilean Meteorological Service (Dirección Meteorológica de Chile, DMC) has implemented the next generation of seasonal forecasts, NextGen‐Chile. This new forecast system is based on a multi‐model ensemble using state‐of‐the‐art general circulation models (GCMs) from the calibrated North American Multi‐Model Ensemble (NMME) project. The forecasts from the GCMs are calibrated using a canonical correlation analysis‐based regression with a homogenised dataset of ground stations. The system is completed with two statistical models built using canonical correlation analysis on sea surface temperature (SST) in the ENSO and the Southwestern Pacific regions. Individually calibrated GCMs and statistical models are combined by weighing their hindcast skill to construct the final calibrated multi‐model ensemble (CMME) prediction. A verification analysis of probabilistic re‐forecasts during 2019–2021 has been performed, adding an average‐based ensemble forecast (CMME‐Mean). The CMME models outperformed the individual models in discrimination and showed less seasonal variability in performance than the individual models, adding consistency to the forecast. All metrics analysed during the verification process were maximised in the central region of Chile, which could be attributed to the high concentration of ground stations in the central region and the definition of a central region‐centred domain for the CCA calculation. Looking into the near future of NextGen‐Chile, a Flexible Seasonal Forecast is introduced as a more comprehensive approach for seasonal forecasts, allowing users and stakeholders to access information beyond the tercile seasonal forecast approach.

Saharan warm intrusions are air masses that develop over the Sahara and that can be advected into surrounding areas, creating anomalous atmospheric conditions in those regions. This paper focuses on the characteristics of these intrusions into the western Mediterranean (WMed) region and their relationship with extreme temperatures in the neighbouring areas during the recent past (1959–2022). We describe and evaluate a methodology to identify Saharan air masses throughout the year, and, consequently, a historical catalogue of intrusion events that reach the WMed is built. To identify which large-scale phenomena might be relevant for the formation of the intrusions, we first identify different intrusion types (ITs) through a clustering procedure. Different ITs are found for the four seasons, which discriminate between the intrusions according to their longitudinal position over the Mediterranean region and their intensity. Upper-tropospheric anomalies are linked to the onset of these events, in particular an anomalous geopotential high over the intrusion region that slows down the upper-tropospheric circulation over northern Africa. These events are very relevant as they impact extreme temperatures throughout the year and account for a high percentage of the extreme-temperature events recorded in the WMed and neighbouring regions in summer.

Timely delivery and effective use of climate information is fundamental for a green recovery and a resilient, climate neutral Europe, in response to climate change and variability. Climate services address this through the provision of climate information for use in decision-making to manage risks and create opportunities. In order to develop future equitable and quality-assured climate services to all sectors of society, it is important to work on developing standardisation procedures for climate services, as well as supporting an equitable European climate services community and also enhancing the uptake of quality-assured climate services to support adaptation and mitigation to climate change and variability. An important component of the climate service reliability and use depends on the data, specifically on the traceable quality of its input and output datasets. The output datasets need to be FAIR, also for machines, meaning the output datasets provide full provenance information about e.g. the input datasets, the processing software, the quality and accuracy, all supported by interoperable metadata, including information on uncertainty. To support those needs and objectives for climate services, we will present preliminary guidelines of good practices for vocabularies, formats, metadata and technical standards, including FAIR principles for data and software/processing. As well, preliminary best practices in provenance of processing methodologies to guarantee traceability and reproducibility of climate data and ways to communicate it in a user-friendly manner will also be shown along with climate uncertainty, risk assessment and communication. Eventually all guidelines and good practices will be shared via the Climateurope2 platform, set up under the project to support the climate services community. Everyone interested can also join our network to actively participate in developing best practices for climate services https://climateurope2.eu/ This project (Climateurope2) has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement N°101056933.

The provision of climate services (CS) has grown at an unprecedented rate over the last decade in response to climate-related risks in several sectors of the global economy; this is especially true in agriculture. Several studies document lessons learnt from (un)successful climate services, and attempt to distil these into key principles, recommendations, or requirements. However, limited systematic analysis and data on the characteristics of the CS that are conducive to success exist to date, including for agriculture. Here, we analyse the Local Technical Agroclimatic Committees (referred to here by its Spanish acronym MTAs) as a CS approach that effectively delivers information to farmers sustainably and at local scale. We propose a framework comprising sixteen metrics that help measure the effectiveness, sustainability, and scalability as key dimensions of CS success. We apply this framework to 26 MTAs across four Latin American countries, namely, Guatemala, Honduras, Nicaragua, and Colombia. The analyses revealed that the MTAs played a significant role in CS transformation pathways, producing a total of 158 outcomes (changes in behaviour of people or institutions), and involving at least 279 institutions at various levels and with diverse roles. Analyses of the sixteen metrics revealed a wide range of performance across the 26 MTAs, with nearly half of the MTAs considered to have or nearly-achieved effectiveness, sustainability, and scalability. MTAs success stems not only from an increase in numbers of farmers and locations reached but also from the evolving roles and responsibilities of a diverse ecosystem of actors that accompany enhanced capacities and tangible benefits on the ground. Based on these results, we propose key CS elements, namely, collaboration; participation; adaptability and flexibility; financial (crowd) resourcing; robust governance and strong leadership; awareness of and improvements in data availability, quality, and assurance; capacity development; user-centred communication; adequate incentives; and enabling policy environment.

One of the critical mechanisms influencing extreme temperatures on a daily basis in the Mediterranean region, particularly during the summer months, involves the intrusion of Saharan or subtropical continental air masses. These warm and stable air masses, which move northward, significantly impact temperatures in the Mediterranean region and have been associated with severe heat waves. This phenomenon has not received extensive attention in the literature despite its impacts. Therefore, understanding these intrusions and the underlying mechanisms driving them is crucial, especially given the observed increase in the frequency of such events in recent decades (1991-2020) compared to the climatology of the latter part of the previous century (1961-1990). In this study, we analyze air masses originating from low-latitude subtropical African desert areas during the historical period using data from the ERA5 observational dataset. We identify intrusion events in the western Mediterranean region through a study of air mass stability (geopotential thickness and vertical mean potential temperature), and we examine their frequency, spatial distribution, persistence, and trends over time. The impacts of these events extend beyond the region of the intrusion, leading to temperature anomalies across the Mediterranean basin and central Europe, indicating the non-local nature of the involved physical mechanisms. Finally, we employ clustering techniques to gain insights into the large-scale weather types responsible for these intrusions and their geographical origins. Specifically, we utilise various applications of the k-means method, using diverse variables and sets of data. The results offer novel insight into the conditions that favour intrusions in different locations of the western Mediterranean, their temporal evolution and their relationship with well-studied teleconnection patterns. It also offers an unprecedented observational climatology to assess the ability of climate models to represent this phenomenon appropriately.

The observed warming in the Western Mediterranean (WM) region during the last decades is projected to continue and grow larger than the global mean, which is why it has been labeled as a climate change hotspot. Even within a relatively small region such as the WM, the combination of natural climate variability and anthropogenic climate change can lead to a large spatial variety of extreme weather and climate events.This study focuses on analysing temperature and precipitation in the WM region at different time scales, using a climate regionalization that considers the WM climatic heterogeneity, emphasising the detection of long-term trends and performing an initial attribution of their sources.The regionalisation process involves the utilisation of monthly temperature and precipitation data from ERA5 during 1950-2020. The regionalisation starts with a pre-filtering of the data with empirical orthogonal functions. A non-hierarchical K-Means clustering was then conducted, followed by a sensitivity analysis to determine the optimal number of clusters, considering the internal representativeness of each group and the seasonal differences between groups. A time-scale decomposition was carried out to disentangle the contribution of different time scales to the regional temperature and precipitation time series of the regions identified. The non-linear, long-term trend component is obtained via regression of the regional temperature and precipitation series with the smoothed global mean-surface temperature anomaly, using the GISTEMPv4 database. The rationale is that precipitation and temperature change not only as a result of time but also due to global warming. To account for the monotonic time-related trend, a Mann-Kendall test is used for trend detection. The decadal "natural" component of the series is obtained as a residual from the trend and filtered using an order-two Butterworth filter with half-power at a period of 10 years. Finally, the interannual component of the series is obtained as a residual from the decadal part.Finally, a preliminary attribution analysis was conducted using data from the Detection and Attribution Model Intercomparison Project (DAMIP) climate simulations to disentangle the contributions of different external factors to the climate system in the observed long-term trends.Results show that most of the variance in temperature series is explained by the long-term trend associated with climate change (~65%), while for the precipitation series, variance is dominated by interannual variability (~60%). These results vary seasonally and spatially throughout the year, with the highest warming trends observed in the Mediterranean coast of the Iberian Peninsula and Africa in summer and the highest drying trends in the southwestern Mediterranean both in summer and winter. The net long-term warming observed in the series is mainly induced by the effect of greenhouse gases, while aerosols provide a smaller cooling effect.

The observed warming in the Western Mediterranean (WMed) region over recent decades is projected to continue, outpacing the global average, making the region a prominent climate change hotspot. Even within this relatively small area, the combination of natural climate variability and anthropogenic climate change creates significant spatial variation in extreme climate events. This study analyzes temperature and precipitation trends in the WMed using ECMWF Reanalysis v5 (ERA5) data from 1951 to 2020 taking into consideration the climatic heterogeneity of the region. A non-hierarchical K-Means clustering method was applied to delineate nine climatically homogeneous regions within the WMed. A timescale decomposition analysis was then conducted to disentangle long-term, decadal, and interannual variability components of seasonal data. Results indicate that long-term trends explain most of the total observed variance in temperature (~65%), while interannual natural variability dominates observed precipitation (~60%). These patterns vary seasonally, with the strongest warming trends along the Mediterranean coast of the Iberian Peninsula and northern Africa in summer and the most significant drying trends in the southwestern Mediterranean during both summer and winter. The influence of different modes of natural climate variability in the observed trends is assessed and appears to contribute to winter drying, but no evidence was found of its influence on warming trends. This study lays a foundation for future climate change detection and attribution efforts in the WMed, emphasizing the need for sub-regional analysis due to the region’s pronounced heterogeneity.

Here we introduce a demand-driven framework designed to implement climate services in the health sector, with a particular focus on the Caribbean region. Climate services are essential for supporting informed decision-making and response strategies in relation to climate-related health risks. Through collaborative efforts, we are co-producing a climate-driven dengue early warning system (EWS) to target vector-borne diseases effectively. While challenges exist in implementing such systems, EWSs provide valuable tools for managing epidemic risks by predicting potential disease outbreaks in advance. The scarcity of operational climate tools in the health sector underscores the need for increased investment and strategic implementation practices. To address these challenges, a demand-driven framework is proposed, emphasizing strategic planning focused on health intervention development, partnership building, data, communication, human resources, capacity building, and sustainable funding. This framework aims to integrate climate services seamlessly into health systems, thereby enhancing public health resilience and facilitating well-informed decision-making to effectively address climate-sensitive diseases.

Climate services are essential to support climate-sensitive decision making, enabling adaptation to climate change and variability, and mitigate the sources of anthropogenic climate change, while taking into account the values and contexts of those involved. The unregulated nature of climate services can lead to low market performance and lack of quality assurance. Best practices, guidance, and standards serve as a form of governance, ensuring quality, legitimacy, and relevance of climate services. The Climateurope2 project (www.climateurope2.eu) addresses this gap by engaging and supporting an equitable and diverse community of climate services to provide recommendations for their standardisation. Four components of climate services are identified (the decision context, the ecosystem of actors and co-production processes, the multiple knowledge systems involved, and the delivery and evaluation of these services) to facilitate analysis. This has resulted in the identification of nine key messages summarising the susceptibility for the climate services standardisation. The recommendations are shared with relevant standardisation bodies and actors as well as with climate services stakeholders and providers.

There is a growing need to understand why the Euro-Mediterranean region and, especially Catalonia, are hotspot regions for both warming as well as drying signals in climate simulations and projections, particularly in summer. Local decision makers call for specific climate information requirements, highlighting the difficulty in having a large range of data sources –observations, global and regional projections, sensitivity and attribution experiments– which lead to discrepancies and analogies regarding the conclusions extracted from different climate data sources. In this context, CLIMCAT is a joint project between the Barcelona Supercomputing Center and the Meteorological Service of Catalonia in which a variety of data-storing, evaluation and visualisation tools are employed to provide user-centred climate indices and filtered future projections. For the latter, a process-based evaluation framework based on atmospheric circulation patterns (CPs) is designed, focusing on capturing the synoptic configurations that dominate the Euro-Mediterranean region and their impacts in Catalonia. The hypothesis behind this research is that better-performing GCMs may present more plausible future simulations in a global warming scenario.CPs are defined using daily mean sea-level pressure (SLP) by means of an Empirical Orthogonal Function (EOF) data reduction combined with Ward's hierarchical clustering. The ECMWF ERA5 reanalysis is considered as reference during 1950-2022 to evaluate a set of 24 global climate models (GCMs) from the Coupled Model Intercomparison Project version 6 (CMIP6). The link between CPs and surface variables –precipitation, maximum and minimum temperatures– is analysed. Model performance is quantified through multiple spatial and temporal metrics, allowing the ranking of the best-performing GCMs. It is found that most of the GCMs are able to capture the annual cycle of the CPs frequency in their historical runs, with a dominant summer CP enhancing warm and dry conditions. However, the correct timing of this pattern and the transitional CPs (autumn and spring) are often misrepresented. The analysis of the surface patterns discriminated by CPs presents an overall good model performance, better for the temperatures than rainfall, particularly in the transition seasons, for which the GCMs spread in their skill score increases. Finally, when combining spatial and temporal skill metrics, we are able to identify the best-performing GCMs over the Euro-Mediterranean region, allowing a filtering of the large set of CMIP6 projections in different future scenarios. This is a flexible workflow that can be easily modified based on user needs, such as emphasising model capabilities in specific variables and/or atmospheric structures, depending on the regional and local needs to reduce model uncertainty. The approach is designed to support the provision of useful and robust climate information that can benefit policy making at a regional scale.

Climate services that rely on the provision of climate forecasts at sub-seasonal, seasonal or decadal time scales (S2S2D) are widely exploited these days. To make them helpful for decision-making, state-of-the-art climate forecast model outputs are tailored to user needs. The conjunction of scientific knowledge and scientific exploration to fulfil users' needs determines the post-processing workflow, from selecting the datasets to visualising the product. Consequently, scientists need to perform different combinations of possible workflows to evaluate the quality of the final products, ensuring the results are meaningful and that best practices (such as cross-validation strategies) are followed. For instance, essential climate variables (ECVs) can be calibrated before computing an indicator, or the indicator based on ECVs could be calibrated instead. Furthermore, several combinations of forecast systems and observation-based datasets can be explored to provide the most convenient product. On the other hand, the required computational resources can be a limitation depending on the total data size involved in the post-processing workflow.To efficiently and flexibly handle all the requirements when exploring and delivering climate services based on S2S2D predictions, the Earth Sciences department of the Barcelona Supercomputing Center has developed the SUbseasoNal to decadal climate forecast post-processIng and asSEssmenT suite, so-called SUNSET. This suite takes advantage of existing software packages and tools (such as startR, CSTools, s2dv, CSDownscale and Autosubmit) to facilitate the definition of the workflow and its parallelisation on HPC machines when available.To tailor climate products for each application and sector (e.g. agriculture, energy, water management, or health), the scientist can decide on the post-processing required steps, such as region selection, regridding method and resolution, anomaly calculation, and downscaling and bias-adjustment methods. SUNSET also allows the creation and visualisation of climate forecast products, such as maps for the most likely tercile. It performs the verification of the products using deterministic and probabilistic metrics, which can be visualised with maps and summarised with scorecards. The storage of final numerical outputs is also designed to consider the need to be ingested by third-party applications or impact models.SUNSET is available in a public repository licensed under the GPLv3 license, and it is under continuous development. The suite is being used in scientific projects such as CERISE, where the next generation of the Copernicus Climate Change Service seasonal climate forecasts are under development.

This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950-2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification fi cation is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations fi gurations significantly fi cantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, inter- annual, and decadal scales, including significant fi cant long-term trends on their frequency. CPs influence fl uence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures the subtropical Azores high leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly fi cantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific fi c user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.