Process-based plant disease models require daily weather inputs, whereas climate information used in agricultural climate services is often available at coarser temporal scales. We evaluated whether stochastic weather generators (WGs) can reproduce daily weather characteristics relevant to process-based disease modeling using GEM and ClimGen with the EPIRICE rice blast model. Using 30 years of observations from five Korean weather stations, 30 independent 30-year synthetic realizations were evaluated for each station and WG. Performance was assessed using conventional weather statistics, quantitative metrics within the 100-day EPIRICE simulation window, EPIRICE-derived area under the disease progress curve (AUDPC), and process-informed diagnostics of infection-conducive weather conditions. ClimGen reproduced temperature more closely, whereas GEM showed lower precipitation RMSE, and relative-humidity performance varied by station and metric. GEM underestimated mean AUDPC at all stations, whereas ClimGen overestimated it, although ClimGen showed smaller absolute mean bias at four of five stations. Both WGs produced reduced AUDPC variability relative to observed-weather-driven simulations. Process-informed diagnostics showed contrasting strengths: ClimGen more closely reproduced infection-conducive temperature conditions and moderate precipitation events, whereas GEM more closely reproduced high-relative-humidity conditions at most stations. No single WG was consistently superior across weather-statistical and disease-model-oriented metrics. These findings demonstrate that WG suitability for process-based disease modeling cannot be inferred from conventional climatological agreement alone and should be assessed using both weather-statistical and impact-oriented criteria before application in forecast-oriented agricultural climate services.
Climate Information Services (CIS) are increasingly recognised as essential tools for enhancing climate change adaptation among smallholder farmers. Yet, the extent to which CIS are available, accessible, usable, and effectively integrated into local farm decision-making in Ghana remains insufficiently understood. This study assessed the effectiveness of CIS in the Mampong Municipality by analysing their availability and accessibility, farmers' perceived impacts on key agricultural decisions, the effectiveness of different dissemination channels, and the determinants of CIS uptake and use. Using a mixed-methods approach involving household surveys (n = 200) and eight focus group discussions, the study found that CIS are widely available and frequently accessed, with 85.5% of farmers reporting that CIS influenced at least one farming decision. Farmers reported that CIS significantly influenced decisions on planting dates (83.0%), crop choices (82.5%), and irrigation practices (66.0%), though gender differences emerged in accessibility and usability. Radio emerged as the dominant and most effective dissemination channel, but women reported lower usability of CIS delivered through mobile phones and community-based extension services. Probit regression results indicated that household size and peer influence significantly affected CIS uptake, while socioeconomic characteristics, farm size, and climate-risk perception were not statistically significant predictors. Overall, farmers perceived CIS as contributing to improved adaptive decision-making, but its effectiveness is constrained by gendered access barriers, household-level constraints, and social influences. Strengthening dissemination approaches, tailoring CIS content to user needs, and leveraging social and institutional structures can enhance CIS utilisation and improve climate resilience among smallholder farmers.
Climate change poses serious challenges to agricultural sustainability in India's semi-arid regions, particularly in intensively cultivated states such as Haryana. Despite growing interest in farm-level climate adaptation, empirical evidence on the non-linear, interactive socioeconomic determinants of adoption decisions in semi-arid South Asia remains limited. This study addresses this gap by analysing crop production-related climate adaptation strategies and their socio-economic determinants among 120 farmers in Karnal district of Haryana, India, using Classification and Regression Tree (CART) analysis and K-means cluster analysis. Farmers were selected through a multistage sampling design comprising equal numbers of beneficiaries and non-beneficiaries receiving climate services under National Innovations in Climate Resilient Agriculture (NICRA) project. Primary data were collected on socioeconomic characteristics, institutional access, and adoption of sixteen climate-resilient practices. Adoption was higher among NICRA beneficiaries for fifteen of sixteen practices, integrated nutrient management being the sole exception. CART identified farming experience, family size, mass media exposure, family education status, age and operational landholding as principal splitting variables. Farming experience most often formed the root node, governing knowledge-intensive practices, whereas family size primarily determined high-yielding varieties, timely sowing, irrigation and zero tillage, with extension contact entering secondarily. Mass media exposure led water management and crop rotation, and landholding contingency cropping alone. Cluster analysis revealed three different farmer typologies- high-adaptation responsive, strategy-specific, and low-adaptation conventional differentiated by the intensity and combination of strategies adopted, highlighting pronounced heterogeneity in adaptive behaviour. These findings provide an empirical basis for designing targeted, typology-specific climate service delivery and extension interventions.
Anthropogenic climate change is increasingly reshaping settlement patterns through rising temperatures and declining bioclimatic comfort, particularly in climatevulnerable regions such as the Mediterranean Basin. Climate pressures often shape internal and gradual mobility rather than longdistance migration. In Türkiye, intensified thermal stress in coastal cities is creating new adaptation pathways linked to elevation. In this context, climate information products, including bioclimatic comfort assessments and climate projections, can provide valuable evidence for understanding emerging mobility responses and supporting climate adaptation planning.This study examines vertical residential mobility from coastal urban areas toward higher-altitude yayla settlements as an emerging, localized form of climate adaptation in Antalya. Adopting a mixedmethods approach, the research integrates bioclimatic comfort analyses, climate projections, landuse and land surface temperature analyses, and semistructured in-depth interviews. The findings reveal a persistent and intensifying thermal differentiation between coastal and highland areas, functioning as a thermal spatial threshold that influences settlement preferences and residential decisions. While yaylas continue to offer relative thermal relief, climate projections indicate continued warming across all elevation zones toward 2100. Qualitative findings show that declining urban comfort, health concerns, and seasonal heat stress are primary drivers of elevationbased residential mobility, while traditional yayla practices are increasingly being transformed into more permanent forms of residence. At the same time, access to vertical residential mobility remains uneven and is increasingly shaped by socioeconomic resources, housing availability, and infrastructure conditions.The study argues that climateinfluenced mobility should be understood not as a direct or crisisdriven outcome of environmental change, but as a socially differentiated and context-dependent adaptation process. Interview findings indicate that changing thermal comfort conditions are influencing residential preferences and contributing to the functional transformation of yayla settlements from seasonal and recreational spaces toward more permanent residential environments. The results further suggest that PETbased bioclimatic comfort assessments, climate projections, and locally grounded evidence derived from household interviews can serve as climate information products that support adaptationoriented decisionmaking by households, planners, and local authorities. By foregrounding vertical residential mobility and bioclimatic comfort as key mechanisms, the study contributes to ongoing discussions on climate adaptation, internal mobility, and the role of climate information in responding to environmental change in climatevulnerable regions.
Persistent extreme heat during both day and night exacerbates health risks, particularly for the elderly. Based on Global Climate Model (GCM) data from the Inter-Sectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b), this study investigates variations in the occurrence of Independent Hot Day (IHD), Independent Hot Night (IHN), and Compound Day-Night Heat (CDNH) and the resulting exposure risk among Asia's elderly population under Shared Socioeconomic Pathways (SSPs). Across all scenarios, CDNH prevalence increases considerably, driving a gradual shift from IHD and IHN toward CDNH and further elevating elderly exposure. During the reference period (1995–2014), CDNH exposure of Asia's elderly population was 6.88 million persons. Under all future scenarios, exposure surges significantly; inter-scenario differences are modest in the near term (2021–2040) but widen sharply thereafter, reaching 211.57 million persons in the middle term (2041–2060) and 830.38 million persons in the long term (2081–2100) under SSP585. High-exposure regions are concentrated in the Ganges Plain, eastern China, and Southeast Asia. Two inequality dimensions are examined: extreme inequality (low-income minus high-income countries) and moderate inequality (low- plus lower-middle-income minus upper-middle- plus high-income countries). Extreme inequality remains persistently negative and intensifies with emissions, indicating greater CDNH exposure burden in high-income countries. Moderate inequality shifts from negative to positive during the mid-to-late century, signaling a reversal whereby the exposure burden increasingly falls on lower-income country groups, which generally possess fewer economic and institutional resources for adaptation.
Extreme precipitation during critical harvest periods is a major source of agricultural losses and damage for smallholder farmers, particularly for crops that are highly sensitive to untimely rainfall. This research investigates how voice-based climate advisory services can support farmer decision-making under rainfall uncertainty during mung bean (Vigna radiata) harvesting in Bangladesh. Using a participatory mixed-methods approach, we co-designed, implemented, and evaluated an automated voice alert system that delivered location-specific five-day rainfall forecasts and harvest advisories directly to farmers' mobile phones. The research combined farmer co-design, rainfall monitoring, advisory dissemination, and post-season telephone surveys to examine farmers' access to, comprehension of, and self-reported responses to advisory messages, as well as their perceived economic value. Farmer responses varied according to the timing of rainfall events relative to harvesting periods and crop maturity. Responses were higher when heavy rainfall occurred during active harvesting periods and crops had reached harvestable maturity, but lower when harvesting had already been completed or crops were still immature. Across multiple seasons, farmers reported perceived avoided losses valued at USD 299–333 ha−1 yr−1. Extrapolation of these farmer-reported estimates to the population of advisory recipients who reported taking protective actions suggested cumulative avoided losses of approximately 1235–3407 t yr−1. The estimated annual value of farmer-reported avoided losses ranged from USD 0.55–1.38 million. These findings suggest that the usefulness of climate advisory services depends not only on forecast information but also on the alignment between weather risks, crop development stages, and available management options.
Road networks face increasing risks from compound climate and traffic stressors, yet practical tools for interpretable, operational assessment remain limited. This study develops a reproducible explainable artificial intelligence framework for network-scale climate risk screening of road infrastructure using Extreme Gradient Boosting and SHapley Additive exPlanations.Models were developed for cracking, roughness, and rutting across 7419 Victorian road links. Performance varied across distress types and classes. Stage-specific diagnostics showed that the rutting model did not reliably discriminate distressed and minority severity classes. Rutting was therefore retained as a reported predictive outcome but excluded from the adopted Unified SHAP aggregation and Climate Risk Formula.The adopted formula integrates the cracking and roughness models within a reduced-form hazard–exposure–vulnerability structure. Global attribution identified Exposure as the largest component (41.1%), followed by Vulnerability (33.1%) and Hazard (25.8%). At the local Climate Risk Score level, mean Aumann–Shapley contributions were highest for Vulnerability (40.1%), followed by Hazard (31.5%) and Exposure (28.4%). Bootstrap and Random Forest sensitivity analyses showed generally stable dominant predictor rankings, although exact component shares remained model-dependent.The resulting scores provide relative, spatially explicit indicators for network screening and prioritisation rather than precise predictions of rare severe distress. The framework offers a transparent and scalable basis for cross-sectional infrastructure climate risk assessment while aligning explainability claims with demonstrated model reliability.
Climate services are crucial for supporting climate informed decision making and achieving sustainable development goals by providing the actionable advisory support, tailored guidance, and user-oriented solutions. These services can also play a pivotal role in shaping household behavior toward the adoption and use of clean energy technologies under climate uncertainty. However, contribution of the climate services to renewable energy engagement and adoption behavior (REEAB) remains under explored in the literature especially at household level. Therefore, the objective of this study was to examine the role of climate services, alongside institutional, psychological and economic factors in REEAB at the household level. Further, the study also investigated the moderating role of perceived effectiveness of climate services between climate services and REEAB of households. The data collected from 515 Chinese urban households through face-to-face cross-sectional survey using multistage random and purposive sampling techniques were analyzed through PLS-SEM. The results revealed that climate services were significantly and positively (β = 0327, p < 0.1) related to household REEAB. Environmental awareness (β = 0.189, p < 0.05), and climate risk perception (β = 0.127, p < 0.1) positively, however, perceived cost burden (β = −0.319, p < 0.1), was negatively associated with the REEAB at household level. Importantly, the perceived effectiveness of climate services significantly moderates the association between climate services and households' REEAB. This depicts that households are more likely to translate climate service delivery into practical renewable energy behavior, when such services are perceived as reliable, actionable, and user-oriented by the households.
Typhoons frequently trigger interacting hazards, including river flooding, urban waterlogging, landslides, and storm surge, but disaster records compiled at the event or administrative-unit level limit systematic analysis of their spatial co-occurrence and environmental differentiation. Here, we developed a multiscale framework to quantify chain-specific disaster-forming-environment (DFE) sensitivity for four predefined typhoon disaster chains: typhoon–rainstorm–urban waterlogging (TRU), typhoon–rainstorm–flooding (TRF), typhoon–rainstorm–landslide (TRL), and typhoon–wind–storm surge (TWS). The results revealed a dual spatial pattern, with widespread landslide-related sensitivity in the mountainous interior and localized flood-, waterlogging-, and storm-surge-related sensitivity in coastal areas. TRL had the largest high-sensitivity area, covering 31.6% of Fujian Province, whereas TRU had the smallest spatial extent but was strongly concentrated in coastal urban districts. TRF and TRU exhibited the strongest positive spatial dependence in urban areas (Kendall's τ = 0.834). Areas highly sensitive to at least one chain covered 42.0% of the province and comprised two broad patterns and nine specific types. Single-chain and compound types accounted for 93.1% and 6.9% of this area, respectively, and TRF + TWS was the most extensive compound type, representing 69.4% of the compound-type area. The spatial organization of these types was scale dependent: TRL became increasingly predominant from county to basin scales, indicating that broader-scale aggregation can obscure localized compound-type heterogeneity. Green-space proportion was the leading environmental discriminator for most flood- and coastal-related types, whereas elevation most strongly differentiated TRL. Types containing a TRU component were generally associated with greater impervious-surface coverage and less green space. These findings reveal contrasting inland and coastal patterns of typhoon disaster-chain sensitivity and provide a basis for multiscale hotspot identification and type-specific disaster-chain management.
Agrometeorological disaster warnings are central to climate services for agricultural risk reduction, but receiving a warning does not necessarily lead farmers to take action. Rather than treating warnings as one-way hazard notifications, this study examines how different warning attributes shape farmers' action thresholds and why responses vary across farmers under conditions of unequal trust and constrained capacity. Using Q-methodology, we conducted Q-sorts and brief post-sorting interviews with 50 farmers in the Guanzhong Plain of Shaanxi, China, an area frequently exposed to drought, heat stress, heavy rainfall, waterlogging, and frost. The analysis identifies four recurring response logics: actionability-first, credibility-gated, constraint-locked, and experience-calibrated/false-alarm-averse. The results show that warnings are more likely to trigger action when they provide clear and prioritized guidance, specify an effective decision window, and are delivered through trusted and consistent channels. However, even credible warnings may not prompt action when labor, machinery, or financial resources cannot be mobilized in time. In addition, repeated false alarms can raise action thresholds unless uncertainty is explained and warnings are followed by reviewable feedback. These findings suggest that agrometeorological climate services should move beyond uniform hazard notification toward decision enablement. Service quality should therefore be assessed not only by forecast accuracy and timely delivery, but also by actionability, trusted communication, feasible lead time, uncertainty explanation, and feedback mechanisms that help farmers integrate warning knowledge into farm-level decisions. Practical implications: Agrometeorological warnings are most useful when they help farmers make decisions, not simply when they inform them that a hazard may occur. Drawing on farmers' responses in the Guanzhong Plain, this study shows that a technically accurate warning may still fail to produce action if farmers do not know what to do, do not trust the source, cannot mobilize resources in time, or have become cautious because of previous false alarms. For climate services to support agricultural risk management, warnings need to be treated as part of a decision-support process rather than as isolated messages.The four action-threshold logics identified here provide a practical way to understand why warnings do or do not enter farm-level decision-making. Some farmers need the warning to clearly explain the action window and the most urgent response. Some need the same message to be confirmed through trusted sources such as official agencies, village organizations, cooperatives, or extension workers. Some understand and trust the warning, but cannot act because labor, machinery, money, or time is insufficient. Others may wait because previous false alarms made them less willing to bear the cost of unnecessary action. These differences mean that a single warning format is unlikely to work equally well for all farmers.For warning providers, the main implication is that climate services should be designed around farmers' action thresholds. A useful service should help farmers answer three practical questions: What risk is expected? What decision needs to be made, and by when? What action is realistic under local conditions? The results support a two-layer warning design. Every alert should first provide a common action base: likely farm consequences, the effective decision window, and the immediate decision or first protective step. A second layer can then provide factor-contingent support, such as operational sequencing, trusted confirmation, resource-matched options, or an explanation of uncertainty and previous forecast performance. Consistency among communication channels remains essential because conflicting messages can delay action even when the forecast itself is reliable.Service quality should therefore be evaluated more broadly than forecast accuracy alone. Accuracy remains essential, but it is not the only condition for effective use. A high-quality agrometeorological climate service should also be understandable, actionable, trusted, timely enough for preparation, transparent about uncertainty, and followed by feedback when warnings are missed, revised, or do not lead to visible impacts. Such feedback is especially important for maintaining long-term trust.These implications also point to the importance of institutional coordination. Meteorological agencies can provide scientific warning information, but farmers often need agricultural extension services, cooperatives, village organizations, and local governments to translate that information into practical action. Extension workers can connect warnings with crop stages and field conditions. Cooperatives and village organizations can strengthen trust and coordinate communication. Local governments can help link warnings with emergency resources such as pumps, machinery, labor, transport, or protective materials.The broader practical message is that climate services become effective when information, institutions, resources, and decision windows are aligned. The goal is not simply to issue more warnings, but to make warning information usable under real farming conditions. In this sense, improving agrometeorological climate services means moving from warning dissemination to action enablement.
This study compared farmer perceptions with four decades (1981–2020) of meteorological observations across two agroecological zones in South Kivu, including the Equatorial High-Altitude Zone (EHAZ) and the Tropical High- and Mid-Altitude Zone (THMAZ). Using a multistage stratified random sampling technique, 450 farmers from three territories (Uvira, Kabare, and Kalehe) were surveyed. Trends were analyzed using the Mann-Kendall test and Sen's slope estimator. Seasonal onset and cessation were determined using agronomic criteria, and climate extremes were assessed using 23 ETCCDI indices. Drought conditions were evaluated using the 12-month Standardized Precipitation Evapotranspiration Index (SPEI). Logistic regression was used to identify the socioeconomic predictors of farmer alignment with these long-term meteorological records. Meteorological analysis revealed significant warming (EHAZ: 0.2 °C/decade; THMAZ: 0.3 °C/decade), but contrasting rainfall trends. EHAZ experienced a significant rainfall decline of 71.5 mm/decade, whereas THMAZ remained stable. Warm days increased by 1.9–4.0%/decade and warm nights by 2.9–4.8%/decade, whereas cool extremes declined proportionally (all p < 0.001) in both zones. Rainy season onset deteriorated in THMAZ for both seasons, where the short rainy season B recorded 20.0% complete failures and 27.5% false onsets over 40 years, compared to minimal failures in EHAZ. Farmer perceptions showed strong directional convergence with temperature (70.9%), extreme heat (66.8%) and drought trends (60.9%) but diverged for annual rainfall totals. While 38.7% aligned with multi-decadal volumetric trends, 34% reported declines in areas where annual totals were statistically stable. Logistic regression identified organizational affiliation, access to weather information, farming experience, and adaptation knowledge as significant predictors of trend alignment. These findings highlight the importance of providing agro-ecologically differentiated climate information, specifically targeting rainfall onset, dry spells, seasonal reliability, and long-term changes rather than simple weather forecasts to support effective local adaptation strategies.
Climate change and rapid urbanization are intensifying natural disaster risks in cities, creating an urgent need for fine-scale assessments that can directly inform resilience planning. Yet existing approaches often fall short in terms of assessment scale, the completeness of risk-element coverage, and their ability to translate rapidly expanding datasets into actionable risk information for governance. To address this gap, we develop a community-based assessment framework that organizes hazard, exposure, vulnerability and coping capacity into a structured modeling process and aggregates the resulting indicators across administrative levels to generate actionable risk information for disaster risk governance. Using 6286 communities in Shanghai as a case study, we show that the framework effectively captures spatial variations in disaster risk and their underlying drivers. Only 8.68% of communities fall into the high overall risk category, primarily located in suburban areas. In terms of risk formation mechanisms, community vulnerability is mainly associated with infrastructure deficiencies and aging population, while insufficient coping capacity stems from low levels of public participation and grassroots mobilization. Classification of dominant risk mechanisms further identifies coping capacity deficit type communities as the most common high-risk type (70.51%). Multi-level aggregation shows that community-level information can be systematically translated into subdistrict and district level profiles, supporting differentiated interventions and targeted resource allocation. Overall, the framework provides a practical pathway for transforming large, heterogeneous datasets into actionable risk information and offers a robust methodological foundation for strengthening disaster risk assessment and urban risk governance.
The intensification of urban heat due to climate change poses risks to both human populations and biodiversity. Urban green spaces (UGS), such as parks and gardens, are typically cooler than surrounding areas, providing respite for residents and habitat for many species. However, whether UGS will maintain sufficient cooling under future warming remains uncertain. This study investigated the microclimate conditions of 15 UGS across three Swiss cities (Zurich, Geneva and Lugano) and evaluated the effectiveness of different adaptation scenarios to maintain their cooling benefits. The Urban Tethys-Chloris (UT&C) model was applied to simulate the universal thermal climate index (UTCI) and surface temperature under historical and future climate conditions. Adaptation scenarios were designed based on the four most influential vegetation parameters on UTCI identified by a sensitivity analysis. Driven by rising air temperatures and a decline in latent heat flux due to reduced precipitation, UTCI was projected to rise by 1.4–4.0 °C and surface temperatures by 1.8–3.2 °C by the 2080s if vegetation remains unchanged. Vegetation-based adaptation cannot fully offset this warming. Trees deliver the largest per-unit cooling and should be prioritized where additional planting is feasible. Where tree expansion is constrained, increasing ground vegetation cover offers a complementary cooling pathway, reducing UTCI by up to 1.4 °C, while denser and taller ground vegetation reduces surface temperature by up to 1.7 °C. We recommend planting dense and tall ground vegetation where tree expansion is limited, to support both human thermal comfort and ground-dwelling species under a warming climate.Practical implications•Green spaces are cooler than built-up areas, providing recreational areas for city residents and serving as refugia for many species. Yet it is unclear whether they will stay cool as Switzerland warms under climate change with longer dry spells (MeteoSwiss and ETH Zurich, 2025). By the 2080s, UTCI in urban green spaces is projected to rise during summer by 4.0 °C in Zurich, 2.8 °C in Geneva, and 1.4 °C in Lugano. This study evaluated the effectiveness of different adaptation measures for green spaces to maintain their cooling benefits.•Vegetation-based adaptation, such as increasing the vegetation coverage and canopy density, can reduce this warming by up to 1.4 °C, but cannot fully counteract future warming. Cities should therefore combine greening strategies with complementary measures such as built-environment changes (cool surfaces, reduced built fractions), blue-green infrastructure (water bodies, permeable pavements), and active water management.•Trees cool more per unit area than ground vegetation and should be prioritized where space allows. In settings where tree planting is constrained by infrastructure, historical preservation, or the need to maintain open public space, increasing ground vegetation coverage is a robust alternative to reduce human heat stress.•When ground vegetation is replaced or expanded, plants that produce a denser canopy (larger leaves, taller stems, higher structural complexity) should be prioritized. Denser, taller vegetation lowers ground surface temperature, which protects ground-dwelling species and biodiversity. Plants with deeper roots should also be planted when possible, to increase proximity to groundwater and limit water stress. Planners should maximize vegetation coverage and canopy density (leaf area, height) enabling faster and simpler implementation.•The cooling achieved by a given vegetation intervention will differ across green spaces, depending on local climate, urban morphology, and green space characteristics. Site-specific assessment is therefore essential when planning adaptation measures.
Wildfires represent a growing threat to Mediterranean landscapes, where socio-ecological factors, land use dynamics and climate variability converge to increase fire risk. This study presents a data-driven approach to wildfire susceptibility and hazard mapping in the Garrotxa region (Catalonia, Spain), identifying the most influential drivers and generating susceptibility maps for present and future conditions. Because georeferenced burned area records exist only for a fraction of the 265 documented ignition events (1900–2023) in the Garrotxa, fire perimeters were reconstructed by simulating spread from each ignition point and its reported size. Validation against fires with real perimeters yielded a mean overlap of 0.71, confirming the reliability of the simulation. These simulated burned areas were integrated with fifteen environmental conditioning factors, whose influence on wildfire occurrence was quantified using the Frequency Ratio method to produce interpretable susceptibility scores. The most influential drivers were wind exposure, land cover, temperature and land surface temperature, while the topographic wetness index and slope exerted weaker effects, consistent with findings from other Mediterranean regions. The resulting map achieved a ROC AUC of 0.73, comparable to a Random Forest benchmark, and 79% of past wildfires fell within the two highest susceptibility classes. Finally, by replacing the climatic conditioning factors with their projected future values, the framework generated wildfire susceptibility maps for the near-, mid- and far-future horizons, showing how fire-prone areas may evolve under climate change. This transferable and interpretable approach reproduces observed fire patterns and supports risk mitigation and climate-adaptive land management in Mediterranean fire-prone areas.
A new observational gridded climatological dataset for daily minimum and maximum temperatures across north-central Italy is described. It spans from 1991 to present, is meant to be operationally updated and covers the territory with a regular grid of about 5 km resolution. The dataset is built starting from observational data collected and quality controlled by the local regional meteorological services.All data are first analyzed to identify those contributing to historical series, covering at least 80% of the total period and passing the SNH test. Interpolation uses in input only data from stations contributing to historical series or from stations representative of meteorological conditions over a large area. The study area is divided into sub-areas, each representing local climatological and geographical characteristics. Interpolation is carried out using a semi-local approach, applied on a daily basis and within each sub-area, using as geographical predictors elevation, urban fraction and water fraction. First, the elevation dependence is analyzed using a non-linear (piecewise) lapse rate model. Then a linear multi-regression model is used to analyze the dependence with respect to the other predictors. Finally residuals are interpolated using a modified Shepard inverse distance weighting algorithm.The dataset climatology is compared with that of ERA5-Land and E-OBS. Furthermore the dataset is described showing daily maps for specific weather events, monthly anomalies, climatological values and trends at annual and seasonal scale, paying particular attention to specific climate indices, derived from daily minimum and maximum temperature data, which can be considered proxies of the frequency of extreme events over the study area.
The demand for climate change services in Australia has grown substantially in recent decades and it is timely to review the national climate services system that has evolved across four key components: (1) data and information, (2) guidance, (3) decision-support tools, and (4) enabling activities such as knowledge brokering and capacity building. This article presents a synthesis of six national and sectoral climate change service case studies in Australia from the past decade: Climate Change in Australia; CoastAdapt; Electricity Sector Climate Information portal; Climate Services for Agriculture; AdaptNRM; and the Australian Climate Service. The aim is to identify national system-level patterns in how climate change services are designed, delivered and used in the Australian context, adding further empirical richness to the emerging literature on national climate services frameworks and systems.The findings highlight strengths and weaknesses. Australian climate change services are characterised by strong scientific foundations, extensive publicly available data and growing adoption across sectors. However, the climate change service ecosystem exhibits fragmentation, uneven governance and variable alignment with user needs. Information is often perceived as complex or difficult to apply, and many services lack sustained funding, coordination and quality assurance mechanisms. Across the case studies, consistent themes emerge relating to the importance of strategic vision, coordinated governance, user-oriented design, investment in enabling capabilities and ongoing monitoring and evaluation. These insights contribute to a better understanding of how national climate change service systems evolve and the factors that influence their effectiveness in supporting decision-making under climate change.
In recent decades, increasingly severe precipitation-driven flood events have been observed across many regions, posing substantial challenges to infrastructure safety and risk management. Reliable estimation of design precipitation associated with rare events is essential for improving flood preparedness and engineering design. In the Chinese mainland, the Pearson Type III (PE3) distribution has been recommended as a unified model for estimating precipitation extremes since 2006. However, this assumption neglects pronounced spatial heterogeneity and may lead to biased estimates of design precipitation, affecting engineering design and risk assessment. In this study, we evaluate 7 commonly used extreme-value distributions for estimating century-scale precipitation extremes in the Chinese mainland, using high-resolution gridded precipitation data from 1961 to 2022. Our results create a composite Regionally Optimal Distribution framework considering 3 algorithms (ROD-3) involving Generalized Extreme Value (GEV), Weibull (WEI), and PE3. The ROD-3 framework provides the optimal fit for over 92% of grids in the Chinese mainland, with GEV best for 54%, followed by WEI and PE3. Compared to PE3, ROD-3 significantly improves goodness-of-fit in 65% of regions and reduces estimation bias for the 100-year return period design precipitation in 22% of regions. In addition, a decision-tree classifier identified a kurtosis threshold (∼0.71) as a simple criterion for distribution selection. Further validation using global land precipitation data reveals a consistent threshold, supporting the framework's transferability across diverse climatic regions. Overall, the framework provides an operational tool for region-specific design standards and risk-informed infrastructure planning, improving the reliability of design precipitation estimation.
Climate services are increasingly recognised as essential tools for supporting decision-making under climate variability and climate change. Although the importance of equitable participation in strengthening collaboration, building trust between climate information providers and users, and supporting more inclusive climate services has already been highlighted, some inequities still remain in the climate services field. This paper integrates methods from Computational Social Sciences to explore the recent evolution and current state of the European climate services landscape. The research process involves the systematic and semi-automated identification, collection, storage, and analysis of qualitative and quantitative data from projects available through the European Commission's CORDIS database. Results reveal that climate services are gaining traction in Europe, illustrated by the increasing funding and number of research projects, many of them primarily focusing on adaptation rather than mitigation. However, the field is currently dominated by academic institutions, with limited involvement from the private sector and low participation from Eastern European countries. These findings are relevant to inform current discussions on supporting an equitable climate services community by describing the current landscape, identifying potential gaps and opportunities for engagement, and demonstrating how Computational Social Sciences methods can support the systematic integration of qualitative textual information into analysis of climate services. Practical implications Climate services are becoming increasingly important in Europe as tools to support decision-making under climate variability and change. Yet, this study shows that the European climate services landscape is uneven in terms of actors involved, regions participating, and topics that receive the most attention, in current research and innovation efforts.The role of the study was to inform the Climateurope2 project in its objective of supporting a more equitable European climate services community by understanding the existing landscape of climate services partnerships within European funding programmes. Mapping these patterns provides an evidence base for identifying potential gaps in participation and informing future stakeholder engagement activities within Climateurope2.Despite the increasing number of projects and funding dedicated to climate services research over time, participation still remains dominated by academic institutions in Western and Southern Europe, with lower representation from private actors and institutions from Central and Eastern Europe. These actors often participate less frequently in EU climate services projects or in more peripheral ways with more limited engagement.Moreover, adaptation clearly appears as the dominant focus in climate services-related projects, while mitigation shows a lower presence in the analysed landscape. While our study does not aim to evaluate whether existing climate services have sufficiently demonstrated benefits for mitigation or for integrated adaptation–mitigation planning, it does reveal a relative separation between these perspectives within the project landscape. We therefore consider this an important area for future research and community-building efforts. Our results suggest that efforts should be directed not only towards increasing participation but also towards broadening the diversity of actors involved in the European climate-services community. This includes bringing the private and public sectors further on board, as well as engaging other communities that have not traditionally been part of the conversation, such as researchers working on climate change mitigation, practitioners from climate-sensitive sectors that do not yet routinely use climate services in their decision-making, and local and indigenous communities.Moreover, this paper demonstrates that project descriptions, abstracts, and other textual materials contain valuable information that is often overlooked. It therefore shows that systematically integrating textual data with quantitative information can inform the climate services field.
Heat early warning systems (HEWS) are critical tools for anticipating hazardous heat and enabling timely response. While some of the earliest systems were developed over two decades ago, little is known about how and why systems have evolved in practice. This study therefore examines the evolution of the Dutch heat warning system from 2007 to 2025 and evaluates the future performance of current warning criteria. We analysed 18 operational documents, including seven post-event evaluations, to identify operational challenges and drivers of change. Current warning criteria were also applied to KNMI’23 climate scenarios to assess future warning frequency, duration, intensity, and season length. Results show that the warning system evolved from a single trigger for the national Heat-Health Action Plan into a tiered warning system, while the HHAP remained linked to lower warning levels, creating misalignment between warnings and actions. Criteria remained primarily climatological due to limited epidemiological evidence, and criteria changes, such as the removal of nighttime criteria, were driven primarily by operational considerations. Future projections indicate a substantial increase in heat warnings, from ~2 per year currently to 3.9–7 by 2050 and up to 29 by 2100. The findings provide lessons for meteorological and public health agencies by showing how operational, institutional, climate, and scientific factors shape the design of heat early warning systems.
Subseasonal forecasts are routinely produced by different prediction centers around the globe, offering actionable lead time for decisions across climate-sensitive sectors. However, the uptake of these forecasts is still limited. We argue that this gap persists not because of a single barrier but because of a set of obstacles: insufficient understanding of how and where subseasonal information enters real decision processes; limited knowledge of when forecasts can be trusted; weak methods for communicating and incorporating uncertainty into operational workflows; and few demonstrated success stories linking forecast use to improved outcomes. In this Perspective, we synthesize the evidence accumulated during the Subseasonal-to-Seasonal Prediction (S2S) Project and identify the critical gaps that limit the transition from prediction science to climate services, and present the research-and-implementation agenda of SAGE (Subseasonal Applications for Agriculture and Environment), a five-year project under the World Weather Research Programme (WWRP). SAGE prioritizes user needs to advance in the understanding on how and where information at subseasonal timescales is and can be used. We outline key scientific questions for the broader community, describe the phased research plan and its expected outcomes. SAGE encourages the entire community of scientists and practitioners to contribute to this agenda.
