Respiratory syncytial virus (RSV) is among leading global causes of lower respiratory tract infections, yet data from Russia and other states of the Former Soviet Union (FSU) remain fragmented and structurally inconsistent. This systematic review aims to map and synthesize existing evidence on RSV epidemiology and genotypic distribution across the FSU. Published studies from eLIBRARY and PubMed databases queried for RSV prevalence data, together with public health surveillance datasets, were used to summarize RSV prevalence research across eight FSU countries. Random-effects meta-analysis across age strata showed high prevalence in children before 6 (21%) and a progressive decline with age, which is in agreement with global data. Prevalence estimates showed a high degree of variability partially explained by study scope and clinical presentation. We observed COVID-19-related seasonal disruptions of RSV seasonality, followed by gradual post-pandemic stabilization. Genotypic data reflects global trends with two cosmopolitan clades, A.D and B.D, and their descendants, dominating in the region. The review is limited by uneven geographical and temporal coverage, and scarce data on adults. The review provides the first integrated summary of RSV epidemiology across the FSU and underscores the need for expanded regional surveillance and genomic reporting.

Abstract Context The ecological implications of multiscale spatial heterogeneity remain poorly resolved in many parts of the ocean, especially at abyssal (3000–6000 m) and hadal (> 6000 m) depths. Seascape ecology offers a framework to link spatial patterns with ecological processes but remains an emerging approach for biodiversity research in the deep sea. Objectives We aim to promote wider recognition of seascape ecology as a unifying framework for understanding biodiversity, spatial patterns, and processes across scales in the deep ocean. Specifically, we aim to identify strategic priorities to advance seascape ecology in abyssal and hadal environments and to transform the framework from concept to practice. Methods We adapt foundational concepts of seascape ecology—Composition, Configuration, Connectivity, and Context —to deep-sea ecosystems across multiple scales. For each, we assess current knowledge, highlight key research gaps, and propose practical avenues for future application. Results & Conclusions Research gaps and priorities are outlined for each concept, as well as an operational workflow. Cross-cutting needs include multi-scale sampling and analysis, integration of abiotic and biotic data, incorporation of traits and phylogeny, improved temporal coverage, and greater technological and methodological standardisation.

High-resolution mapping of allergenic pollen risk across China using ensemble machine learning.

Abstract. Rapid urbanization and industrialization affected the air quality in the Philippines. Fine particulate matter (PM2.5) are of particular concern due to their health, environmental, as well as climate effects. Due to the lack of active and available air quality monitoring in the Philippines, air quality monitoring and mitigation cannot be performed. Satellite air quality data can be utilized to provide extensive spatial and temporal coverage. In this study, aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) onboard the GEO-COMPSAT-2B satellite was used to estimate PM2.5 and compared with data from a ground monitoring station in Manila, Philippines along with meteorological data from the European Centre for Medium- Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Random forest (RD), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were evaluated for their accuracy in predicting ground-level PM2.5. SVM achieved the highest accuracy (R2: 0.998) followed by RF (R2: 0.997), and then XGBoost (R2: 0.673). SHAP analysis showed that wind speed has the highest contribution in predicting PM2.5 This study shows that satellite air quality data can be used for ground-level PM2.5 estimation.

Simple to use and accurate monitoring of stroke patients’ breathing and heart rate in sleep is needed to lower the risk of secondary strokes and prevent worse functional outcomes due to disturbed sleep. In this study, we computed breathing and heart rates from clinical radar sleep recordings of 49 acute ischemic stroke patients. Parallel polysomnography served as the reference for evaluation. We compared radar rates computed using previously developed multiple and single range bin selection methods. Multiple selection yielded lower mean absolute errors (breathing: 0.39 vs. 0.87 breaths per min; heart rate: 0.84 vs. 3.99 beats per min) and higher correlations with the reference (breathing: 0.95 vs. 0.85; heart rate: 0.96 vs. 0.56). However, single range bin selection produced rates for a larger proportion of recording time (breathing: 93.49% vs. 73.38%; heart rate: 81.85% vs. 19.93%). Our results indicate that multiple range bin selection provides more accurate estimates of breathing and heart rate, but it has lower temporal coverage. Easy to use radar systems could facilitate the clinical adoption of contactless breathing and heart rate monitoring in sleep, improving the care provided to stroke patients.

Coastal sandbars play a crucial role in shoreline protection, yet monitoring their dynamics remains challenging due to the cost and limited temporal coverage of traditional surveys. This study assesses the feasibility of using Sentinel-2 multispectral imagery combined with the logarithmic band ratio method to automatically detect submerged sandbar crests along three morphologically distinct beaches on the northwestern Mediterranean coast. Pseudo-bathymetry was derived from log-transformed band ratios of blue-green and blue-red reflectance used to extract the sandbar crest and validated against high-resolution in situ bathymetry. The blue-green band ratio achieved higher accuracy than the blue-red band ratio, which performed slightly better in very shallow waters. Its application across single, single/double, and double shore-parallel bar systems demonstrated the robustness and transferability of the approach. However, the method requires relatively clear or calm water conditions, and breaking-wave foam, sunglint, or cloud cover conditions limit the number of usable satellite images. A temporal analysis at a dissipative beach further revealed coherent bar migration patterns associated with storm events, consistent with observed hydrodynamic forcing. The proposed method is cost-free, computationally efficient, and broadly applicable for large-scale and long-term sandbar monitoring where optical water clarity permits. Its simplicity enables integration into coastal management frameworks, supporting sediment-budget assessment and resilience evaluation in data-limited regions.

The caldén (Neltuma caldenia) forest, a xerophytic low-stature ecosystem in central Argentina, faces increasing threats from land use change and desertification. This study assesses the capability of full-polarimetric L-band SAR data from the Argentine SAOCOM-1A satellite to characterise forest attributes in this ecosystem. We computed the Generalised Radar Vegetation Index (GRVI) and compared it with aboveground biomass and tree canopy cover data from the Second National Forest Inventory, under fire and non-fire conditions. We also assessed other SAR indices and polarimetric decompositions. GRVI values exhibited limited variability relative to the broad range of field-estimated biomass, and most regression models were not statistically significant. Nevertheless, GRVI effectively distinguished woody from non-woody vegetation and showed a weak correlation with canopy cover. Statistically significant, albeit weak, correlations were also observed between biomass and specific polarimetric components, such as the helix term of the Yamaguchi decomposition and the Pauli volume component. Key challenges included limited spatial and temporal coverage of SAOCOM-1A data and the distribution of field plots. Despite these limitations, our results support the use of GRVI for land cover monitoring in semiarid regions, emphasising the importance of multitemporal data, integration with C-band SAR, and enhanced field sampling to improve forest attribute modelling.

Total Electron Content (TEC) maps allow the evaluation of the state of the ionosphere. There are many providers/sources of worldwide or regional TEC maps for the continuous monitoring of the ionosphere, which employ different GNSS monitoring networks for data acquisition, TEC calculation or interpolation methods for generating the maps, or different spatial and temporal resolutions and coverage. How reliable are TEC maps over Brazil? We employed TEC maps from four different providers for 2022–2024, in the growing phase of the current solar cycle 25. Seasonality is also taken into account. A systematic comparison of TEC maps over Brazil was performed using correlation and similarity analysis between maps of different sources. Significant differences were found. Even for the same source there are differences in the density of monitoring stations according to the region. An example of bubble signature in TEC maps is also analyzed. Ground truth validation of TEC is performed by comparing TEC point values extracted from the maps with values derived from a set of GNSS stations over Brazil. As a result, no TEC maps of these sources were deemed reliable, due to low spatial and/or temporal resolution, low monitoring station density, or inadequate interpolation scheme.

Abstract Juno in situ observations provide insightful data on plasma, electric, and magnetic fields associated with Jovian decametric (DAM) emission, while remote-sensing observations offer broader spatial and temporal coverage. Combining both perspectives may help us understand the dynamic processes of DAM radiation. In this work, we present an Io-related DAM emission continued intermittently for at least 2 hr, observed by Juno in situ and remotely by Wind and STEREO-A. The Io-DAM emission evolved from three discrete narrow arcs to one mixed broad arc within 11 minutes in remote-sensing dynamic spectra. The source remained near a lead angle of 6° from the main Alfvén wing spot during the observation. Juno/JADE detected peaks in electron energy flux and an upward loss-cone distribution. Using a set of resonance circles, we estimate a maximum growth rate γ / ω c of 6.4 × 10 −4 , corresponding to electron energies of 0.2–10 keV and emission angles of 77°–88°. Meanwhile, we infer the properties of the source region of the event from the remote-sensing observations at 1 au. The inferred results on electron energy, emission angle, and source locations are consistent with the Juno in situ observations, suggesting the consistent properties of this Io-DAM event over 2 hr observational intervals. This consistency also reinforces the reliability of the remote sensing inversion method. Our work contributes to the establishment of long-term stereoscopic remote monitoring, addressing limitations in local observational coverage and enhancing our understanding of the dynamic processes of DAM emission.

Abstract Contrails are a significant contributor to aviation's climate impact with an effective radiative forcing similar to that from aviation's emissions, yet large uncertainties remain. Many observational contrail studies rely on data from a single sensor, in recent years increasingly from a geostationary imager, accepting lower spatial resolution in exchange for higher temporal and spatial coverage. However, the ability of geostationary imagery to resolve contrails has not been systematically characterized. By comparing higher spatial resolution low Earth orbit satellite imagery from Visual Infrared Imaging Radiometer Suite (VIIRS) to geostationary satellite imagery from GOES ABI, we show that the latter does not resolve 80% of the contrails nor half of the total length compared to contrails identified with VIIRS. Our findings underscore the need for multi‐sensor approaches to collect observational contrail data for improved validation of climate models and to enable more rigorous and verifiable contrail avoidance strategies.

The aviation sector is one of the largest sources of greenhouse gas emissions, and the European Union (EU) is calling for a rapid transition to sustainable aviation fuels (SAFs). This study aims to assess market dynamics and regulatory challenges of sustainable aviation fuels (SAFs) in the European Union, with emphasis on economic feasibility and the role of policy frameworks. Using econometric methods: Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models, forecasts of SAF infrastructure development trajectories were produced, while regression analysis was applied to assess the relationship between national GDP and the scale of SAF deployment. The results revealed a statistically significant positive link between higher economic development and faster expansion of SAF infrastructure, highlighting the policy-driven nature of market dynamics. Germany and France demonstrate the greatest growth potential, while countries such as Italy and Denmark show slower progress. The findings confirm that clear regulatory frameworks and targeted economic incentives are essential to stimulate SAF uptake; however, additional investment and stronger policy harmonization across Member States are required to achieve large-scale commercialization and long-term sustainability. The empirical analysis utilizes data from 2015 to 2023 to estimate SAF infrastructure trajectories and policy effects, ensuring sufficient temporal coverage for robust econometric modeling and forecasting.

Geothermally heated hot springs often serve as reservoirs for diverse microorganisms, including pathogenic Gram-negative bacteria and potential carriers of antimicrobial resistance (AR). Thus, this study determined the microbial concentration and AR profiles of Gram-negative bacteria isolated from selected recreational hot springs in Calamba, Laguna, Philippines. The physicochemical parameters of the water were also analyzed to determine their correlations with the bacterial AR profiles. From 80 water samples collected over two months (March and April) in 2025 from four hot springs, the estimated average concentrations of Escherichia coli, total coliforms, and non-coliforms (in CFU/100 mL) were 93, 246, and 1,490, respectively. Two sampling sites failed to meet the Class B standards for primary contact recreation, as specified in the 2015 Guidelines for Recreational Waters Monitoring Program of the Philippine Department of Environment and Natural Resources. Moreover, antimicrobial susceptibility testing of 80 isolates against 21 antibiotics revealed low resistance rates, with key correlations identified between AR and environmental variables such as water temperature, pH, dissolved oxygen (DO), and conductivity. These findings suggest potential environmental influences on AR dynamics. However, the study's limited geographic scope and temporal coverage highlights the need for future longitudinal and broader geographic investigations. Nevertheless, the study provided baseline data for monitoring AR in recreational hot springs. It contributed to the understanding of AR ecology, offering insights for policymakers and researchers to design evidence-based strategies that mitigate the potential risk of contamination from runoff, maintain water quality standards, and safeguard public health.

Continuous global monitoring of volcanic activity from space requires balancing spatial and temporal resolution, a long-standing trade-off between polar-orbiting and geostationary satellites. Polar sensors such as MODIS, VIIRS, and SLSTR provide high spatial resolution (375 m–1 km) but with limited temporal coverage. In contrast, geostationary sensors like SEVIRI offer high temporal resolution (5–15 min) but with coarser spatial detail (~3 km), often missing lower-intensity thermal events. The recently launched Flexible Combined Imager (FCI) aboard the geostationary Meteosat Third Generation (MTG-I) satellite represents a major improvement, providing images every 10 min with a spatial resolution of 1–2 km, comparable to that of polar orbiters. Here, we adapted the established Remote Sensing Data Fusion (RSDF) algorithm to exploit the enhanced capabilities of FCI for detecting volcanic thermal anomalies and estimating Volcanic Radiative Power (VRP). The algorithm was applied to Mount Etna during three different eruptive phases that occurred in 2025. The VRP derived from FCI data was compared with that obtained from the geostationary SEVIRI and the polar-orbiting MODIS, SLSTR, and VIIRS sensors. The results show that FCI provides a more detailed and continuous characterization of volcanic thermal output than SEVIRI, while maintaining close agreement with polar sensors. These findings confirm the capability of FCI to deliver high-frequency, high-resolution thermal monitoring, representing a major step toward operational, near-real-time volcanic surveillance from space.

Epilepsy patients face significantly elevated cardiovascular risks, with cardiac arrhythmias occurring 2-3 times more frequently than in the general population. Current knowledge of brain-heart functional coupling abnormalities in epilepsy, particularly during interictal periods, remains limited. We investigated brain-heart interplay characteristics in temporal lobe epilepsy through synchronized electroencephalographic-electrocardiographic analysis using a synthetic data generation model and microstate analysis. We enrolled 52 patients with temporal lobe epilepsy (mean age = 33.4 ± 12.7 years) and 42 age-matched healthy controls (mean age = 31.95 ± 11.03 years). Twenty-minute artifact-free electroencephalographic segments were analyzed during resting states. Four directional brain-heart coupling sequences were extracted: CBrain→HF, CBrain→LF, CHF→Brain, and CLF→Brain, representing bidirectional interactions between brain activity and cardiac components. Six microstate topologies were consistently identified across all brain-heart interplay sequences, with temporal lobe epilepsy patients demonstrating significantly more complex and unstable topological characteristics compared to healthy controls. For CBrain→HF coupling, patients exhibited significantly reduced mean duration of microstate 3 (.34 ± .09 s vs. .38 ± .07 s, p = .02), increased occurrence rates of microstates 3 and 4 (both p < .001), and altered temporal coverage patterns. Similar abnormalities were observed across all sequences, with patients showing shortened microstate durations, altered occurrence rates, and disrupted temporal coverage. Spatial dissimilarity analysis revealed significant topological abnormalities across all microstates. A logistic regression model incorporating microstate parameters achieved 94.7% diagnostic accuracy for temporal lobe epilepsy, with an F1 score of .952 and an area under the curve of .932. Temporal lobe epilepsy is characterized by profound disruptions in brain-heart functional coupling during interictal periods, manifesting as altered microstate topographies and temporal dynamics. These findings establish microstate-based analysis as a promising framework for characterizing brain-heart axis dysfunction in epilepsy.

ABSTRACT Crop-type maps are critical for addressing food insecurity, yet national-scale, high-resolution maps of staple crops like wheat remain unavailable for many African countries due to limited, publicly available ground reference data. Ethiopia's complex and fragmented agricultural smallholder landscape and frequent cloud cover during rainfed seasons make crop mapping particularly challenging. This study presents the first national-scale, high-resolution wheat area map for Ethiopia, generated through a satellite-based workflow integrating (i) gap-filled, cloud-masked Sentinel-2 time series; (ii) Random Forest classification using temporal-spectral profiles and the EthCT2020 reference dataset; (iii) cropland masking; (iv) multistep validation; and (v) confidence-based consolidation with sub-national statistics. The resulting map estimates approximately 3.19 million hectares of wheat for the 2020/21 rainfed season, with 10 m spatial resolution and temporal coverage from April to December 2020. The dataset, openly accessible via CIMMYT Dataverse, provides a critical resource for operational wheat disease early warning and advisory systems in Ethiopia.

This study presents a 13-year (2012-2024) assessment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments from the Serbian Danube River (rkm 1112-864). A total of 132 sediment samples were collected during spring and autumn at seven sites, extracted via Accelerated Solvent Extraction (ASE), and analyzed by GC-MS. Total concentrations of 16 priority PAHs (Σ16PAHs) ranged from 33.4 to 1093.4µg/kg in spring and 32.0-2747.4µg/kg in autumn, with seasonal averages of 203-467µg/kg and 269-561µg/kg, respectively.Four-ring PAHs accounted for 49-81% of total PAHs, and carcinogenic PAHs contributed up to 61% of Σ16PAHs. The highest toxicity equivalent (TEQ) value of 458.3µg TEQ/kg (S2, September 2016) remained below the Canadian guideline of 600µg/kg. The sum of 10 PAHs was predominantly below or near the Serbian target value of 1mg/kg, with autumn exceedances observed at S1 (1260µg/kg) and S2 (2317µg/kg). However, all values remained well below the maximum permissible limit of 10mg/kg. Diagnostic ratios (BaA/(BaA + Chr), Flu/(Flu + Pyr), LMW/HMW) and the dominance of high-molecular-weight PAHs indicate predominantly pyrolytic sources, mainly coal and biomass combustion. TEQ values declined after 2019, suggesting reduced emissions. Overall, sediments are classified as low to moderately contaminated, with low to moderate ecological risks. This study provides updated long-term data for the central Serbian section of the Danube River, addressing a knowledge gap resulting from the limited temporal and spatial coverage of previous PAH studies, and highlights the importance of systematic monitoring and assessment of individual PAH distributions to evaluate trends and ecological risks.

Soil moisture is a critical component of the Earth's energy and water cycles. However, most existing products focus solely on surface layers, and continuous, high-resolution datasets for deep soil horizons remain scarce. To address this gap, we generated a global, daily, seamless multilayer soil moisture dataset (SWSM) for the period 2002-2021 by leveraging a machine learning approach (XGBoost). The SWSM dataset provides estimates at a 0.05° spatial resolution for three depth horizons: 0-10 cm, 10-30 cm, and 30-60 cm. Rigorous validation against in situ observations demonstrated the dataset's high accuracy, with Pearson correlation coefficients exceeding 0.90 and root mean square errors below 0.05 across all depths. A feature importance assessment verified the dataset's physical consistency, revealing depth-dependent patterns aligned with established hydrological understanding. The SWSM dataset, with its long-term temporal coverage, fine spatial resolution, and multi-layer structure, is a valuable resource for applications in hydrologic modeling, agricultural water management, and climate change studies.

Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R2 = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques.

Abstract. Atmospheric sulfate formation influences climate and air quality, yet its chemical pathways remain difficult to constrain. This study utilizes the oxygen isotope anomaly (Δ17O) of sulfate aerosol (ASO4) as a tracer to distinguish formation processes. This work presents a simulation of Δ17O(ASO4) within the contiguous United States, conducted over full annual cycles, which enables the quantification of seasonal and spatial patterns of sulfate oxidation pathways and their response to major emission reductions, for the first time at this scale and temporal coverage. In 2019, Δ17O(ASO4) values were predicted to be below 1 ‰ in the Gulf Coast, indicating acidic, ASO4-rich conditions dominated by S(IV) + H2O2 oxidation, while values above 2 ‰ in the West suggested less acidic conditions, leading to enhanced ASO4 production via S(IV) + O3 oxidation. Peak Δ17O(ASO4) values of ∼4.5 ‰ in April across the Western US reflected O3-driven ASO4 formation during high ammonia (NH3) emissions from fertilization. Between 2006 and 2019, mean Δ17O(ASO4) was predicted to increase by up to 2 ‰, driven by declining sulfur dioxide (SO2) emissions from regulatory measures. Model comparisons with historical measurements show reasonable agreement in the acidic southeastern US (RMSE = 0.20 ‰, Baton Rouge, LA). However, the model overpredicts Δ17O(ASO4) in the Western US with RMSE values of 0.36 ‰ (La Jolla, CA) and 1.9 ‰ (White Mountain Research Center, CA). This overestimation suggests an excessive model response to aqueous S(IV) + O3 reactions. These findings underscore the diagnostic potential of Δ17O(ASO4) for assessing sulfate formation mechanisms and pinpointing shortcomings in chemical transport models. However, Δ17O(ASO4) observations across the United States remain exceedingly limited, with most available data dating back to the late 1990s and early 2000s, highlighting the need for renewed measurement efforts.

Abstract. This work presents Mexico's High Resolution Climate Database (MexHiResClimDB), which is a newly developed gridded, high-resolution climate dataset comprised of daily, monthly and yearly precipitation and temperature (Tmin, Tmax, Tavg). This new database provides the largest temporal coverage of the aforementioned climate variables at the highest spatial resolution (20 arcsec, or 560 m on Mexico's CCL projection) when compared to the other currently available gridded datasets for Mexico and its development has allowed for the analysis of the country's climate extremes for the 1951–2020 period. By comparing the spatial distribution of precipitation from the MexHiResClimDB with other gridded data (Daymet, L15, CHIRPS and PERSIANN CDR), it was found that the precipitation provided by this new dataset adequately represents the spatial variation of extreme precipitation events, in particular for the precipitation that occurred during 15–16 September 2013, caused by the presence of Tropical storm Manuel in the Pacific Ocean and Hurricane Ingrid (Cat 1) in the Gulf of Mexico. Using data from 61 days retrieved from Automated Weather Stations located throughout Mexico – and correspoding to the two months with the largest precipitation in Mexico – it was found that precipitation data from MexHiResClimDB has the lowest MAE (8.7 mm), compared to those of L15 (9.5 mm), Daymet (10.1 mm) and CHIRPS (11.7 mm). For Tmin and Tmax, the lowest MAE was obtained with MexHiResClimDB (1.7 and 1.8 °C, respectively), followed by Daymet (2.0 °C for both temperatures) and L15 (2.4 and 2.5 °C). With this new database an analysis of the extreme events of precipitation and temperature in Mexico for the 1951–2020 period was undertaken: the wettest year was 1958, the wettest day 26 September 1970, and September of 2013 the wettest month. It was also found that eight out of the ten days with the highest Tmin occurred in 2020, the two months with the highest Tmin were July and August of 2020 and that the six years with the highest Tmin were 2015–2020. When Tmax was analysed, it was found that the hottest day was 15 June 1998, while June of 1998 was the hottest month and 2020 the hottest year, and that the four hottest years occurred between 2011–2020. Nationwide (and considering 1961–1990 as the baseline period), Tmin, Tavg and Tmax have increased, with their anomalies drastically increasing in recent years and reaching values above 1.0 °C in 2020. At the same time, precipitation has also decreased in recent years – which combined with the increase in temperature will have severe impacts on water availability. This new database provides a tool to quantify – in detail – the spatio-temporal variability of climate throughout Mexico. The MexHiResClimDB entire dataset is available on Figshare (https://doi.org/10.6084/m9.figshare.c.7689428.v2, Carrera-Hernández, 2025a).