Flooding in the Chao Phraya River (CPY) basin has become increasingly severe and frequent, posing risks to agriculture, livelihoods, and the economy. This study assesses the hydroclimatic extremes in the CPY basin and their implications for future flood magnitude and frequency, emphasizing flood volume rather than peak discharge as the primary risk indicator. Climatic data from 36 stations are analyzed, along with future projections from 30 climate models, under the SSP2-4.5 and SSP5-8.5 scenarios. A multilayer perceptron (MLP) model is developed to simulate monthly streamflow for future scenarios. The trained model reproduced observed streamflow satisfactorily during both the training and testing periods (R2 and NSE > 0.75). The MLP provides a computationally efficient framework for estimating future flood magnitudes, enabling rapid flood risk assessment across large basins. Despite only a modest projected increase in annual rainfall (4–6
In densely populated industrial cities, residents experience heightened exposure to air pollutants, raising environmental justice concerns and impeding progress toward Sustainable Development Goals (SDGs). This study examines seasonal air pollution patterns in four major industrial cities of Bangladesh using Sentinel-5P TROPOMI data. The analysis reveals distinct seasonal trends, with pollutant levels lowest during the monsoon and highest in winter and post-monsoon periods, while some pollutants also peak pre-monsoon. Hotspot analysis identifies severe CO pollution in winter across central, northern, and southern Dhaka, while NO2 levels are notably high in Dhaka, Narayanganj, and Gazipur during post-monsoon and winter. Geographically Weighted Regression (GWR) analysis highlights key relationships between pollutants and meteorological variables. O3 shows a strong positive GWR coefficient with precipitation in Gazipur during monsoon (0.79 to 2.15) and a negative coefficient with temperature in Narsingdi during pre-monsoon (–2.23 to − 1.88). NO2 shows a positive coefficient with temperature in Gazipur and Dhaka during monsoon (1.02 to 1.47) and a negative coefficient with precipitation in Narsingdi during pre-monsoon (–1.66 to − 1.38). Other pollutants, including HCHO, SO2, and CO, exhibit varying relationships with humidity, pressure, temperature, wind speed, and precipitation across seasons and locations. Workers and residents near industrial zones face significant health risks, underscoring environmental justice concerns. This study highlights air pollution’s detrimental impact on SDGs 3, 7, and 11, obstructing sustainable development. The findings can inform Bangladesh and other industrialized nations in identifying pollution sources and implementing effective mitigation strategies for healthier, more sustainable urban environments.
Monitoring of the coastal environment is very important in determining the quality of water and stability of the ecosystem in the face of environmental stressors. The implementation of the heterogeneous marine sensors gives rise to multi-sensor time-series information that is synchronized and nonlinear in nature, with inter-sensor relationships, noise, and missing data. Traditional statistical and single-sensor machine learning methods are frequently not sufficient to describe multivariate Statistical relations and long-term temporal relations of such data. In order to tackle these issues, this paper presents a multi-sensor time-series classification end-to-end deep learning system in a coastal environmental monitoring environment. The methodology uses raw instrument-level readings on the Open Marine Stream data and uses a dynamic graph structure to model statistical relationships between physically distinct sensor streams. A Graph Neural Network (GNN) is used to extract Relational features that are used to represent inter-sensor dependencies, and a Gated Recurrent Unit (GRU) is used to reflect temporal dynamics represented by a sequence of successive time windows. At last, the Multi-Layer Perceptron (MLP) will classify the environmental conditions into four levels of severity: Normal, Low, Medium and High. The experimental findings indicate that the integrated GNN-GRU-MLP framework is effective in learning general Temporal relational representations, which show an overall classification accuracy of 97.25
Accurate precipitation estimates are essential for hydrological modeling and flood forecasting in arid regions such as Egypt, where rain-gauge observations are sparse. This study evaluated five high-resolution rainfall products against daily records from 23 rain-gauge stations across Egypt's hydrological zones during 2003–2024. The evaluated products were Climate Hazards Group InfraRed Precipitation with Station Data version 2.0 (CHIRPS V2.0), Climate Prediction Center Morphing Technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM Final Run version 07 (GPM IMERG-F V07), and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5). Three bias-correction methods were tested: dense neural network (DNN), quantile mapping (QM), and linear scaling (LS). A feedforward DNN was optimized separately for each station, with 2–6 hidden layers, 16–256 neurons per layer, 150–300 training epochs, batch sizes of 16–128, learning rates of 0.0005–0.0050, and dropout rates of 0.0–0.2. Results showed that CHIRPS V2.0 generally produced the lowest root mean square error (RMSE) at the daily and monthly timescales, whereas ERA5 and GPM IMERG-F V07 performed better at the yearly and maximum-yearly timescales. In addition, GPM IMERG-F V07 showed reliable rainfall-event detection across Egypt, with probability of detection (POD) values exceeding 0.45. ERA5 also performed well in detecting rainfall events at several northern stations. DNN-based bias correction substantially improved rainfall estimates, reducing the error by 33
The pre-monsoon (March to May, MAM) rainfall over Kerala, a southern state of India, has significant increasing trend in the recent decades. The pre-monsoon period is experiencing stronger warming in the North Indian Ocean (NIO) and associated westerly wind flow to Southern India, leading to increased moisture transport to the Arabian Sea (AS), South India, and the Bay of Bengal (BoB) resulting increase in large scale rainfall. The strong pre-monsoon rainfall years are associated with a La Niña pattern in the tropical Pacific, aided by increased southwesterly flow over the Kerala region from the AS. Embedded in this basic state, convective rainfall makes a major contribution in the strong years with a greater number of active days. The number of extremes intensifies during strong pre-monsoon years, indicating that regional-scale changes are a major contributor to pre-monsoon rainfall and its increasing trend. The weak pre-monsoon years have El Niño anomalies in March and April, while May has weak La Niña cooling, indicating a transition phase with weak cross-equatorial flow along with suppressed regional-scale activity. Onset evolution indicates that during strong pre-monsoon years, pre-monsoon rainfall shows a brief pause for 5–6 days before onset, resulting in an increase in SST in the south BoB and AS. These years are associated with the formation of low-pressure systems in the BoB and AS, which move to the southeastern and northwestern parts of India, respectively, initiating organised convection and its northward propagation. Meanwhile, during weak pre-monsoon years, the low-pressure system forms only in the BoB and then recurves eastward into Bangladesh.
Identifying the sources of water vapor and the associated stable water isotope fractionation is essential for understanding the modern hydrological cycle, particularly under arid climatic conditions. Using precipitation isotope data collected from six sites in Tianshui between 2019 and 2024, we analyzed the spatiotemporal variations of δ1⁸O and d‑excess. Air mass transport trajectories were simulated using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model), while potential evaporation source regions were identified via the Potential Source Contribution Function (PSCF) and Concentration‑Weighted Trajectory (CWT) methods. The local meteoric water line (LMWL) for Tianshui is δD = 7.49 δ1⁸O + 7.15 (r2 = 0.94). The δ1⁸O in precipitation shows a significant positive correlation with temperature and convective activity (r2 = 0.54) and a negative correlation with relative humidity (r2 = − 0.52). Seasonally, δ1⁸O is higher in summer and lower in winter, whereas d‑excess exhibits the opposite pattern. Three primary moisture sources are identified: westerlies (43.54
Agricultural drought (AD) is a critical phenomenon that severely impedes crop productivity and global food security. While Deep Learning (DL) architectures have demonstrated significant potential in modeling soil moisture dynamics, existing methodologies frequently overlook inherent stochastic noise in multi-source datasets and the complex non-linear, often non-positive, correlations between hydroclimatological predictors and target variables. To address these challenges, this study proposes a novel Feature and Temporal Attention Extraction LSTM (FAELSTM) framework. The core of this architecture is a dual-attention (FAE) module designed to adaptively reweight hydroclimatological variables to mitigate data noise, coupled with a Bidirectional LSTM (BiLSTM) backbone to capture long-range spatiotemporal dependencies. Leveraging a long-term, high-resolution ERA5-Land dataset across China, we selected 11 hydroclimatological variables to serve as predictors for forecasting the Soil Moisture Condition Index (SMCI) over a specified future horizon. These variables include 2 m temperature (t2m), 10 m u/v-component of wind (u, v), precipitation (pre), surface pressure (ssr), specific humidity (spec), surface downward solar radiation (ssrd), surface downward thermal radiation (strd), soil temperature level 1 (stl1), total evaporation (e), and soil water capacity (swc). Experimental results demonstrate that FAELSTM consistently outperforms various state-of-the-art baselines. The model exhibits enhanced predictive precision and superior robustness, particularly in navigating class imbalance and data skewness to accurately identify severe and extreme drought stages where traditional architectures often falter. Furthermore, feature interpretability analysis confirms that the framework autonomously prioritizes key physical drivers—aligning with established hydrological water balance principles. This study validates the effectiveness of the FAELSTM framework in processing complex land-atmosphere coupled systems, providing a reliable decision-support tool for proactive agricultural water management and disaster mitigation.
The skillful decadal predictability of the Indian Summer Monsoon Rainfall (ISMR) is of paramount importance for agriculture, economy, and water resource management over the Indian region. This study demonstrates the potential of the Decadal Climate Prediction Project (DCPP) hindcasts in enhancing the predictability of the ISMR on a decadal time scale with the help of improved ocean initializations. For this purpose, a comparison is made between the skills of the DCPP initialized hindcasts and the Coupled Model Intercomparison Project Phase 6 (CMIP6) uninitialized simulations. A significant increase in the decadal prediction skill of the ISMR is noticed over the northwest, west-central, central, and northeast regions of India. It is found that the DCPP hindcasts of the ISMR are overconfident on a decadal time scale. The correlation between individual ensemble members is much better than their corresponding correlations with the observations. We also examine the relative roles of initialization and external forcing in improving the ISMR decadal prediction skill. The short-term (e.g., decadal) climate projections from coupled climate models primarily focus on simulating the climate response to natural and anthropogenic external forcings (including solar variability, volcanic eruptions, changes in greenhouse gas concentrations, and anthropogenic aerosols etc.), while treating internal climate variability as largely chaotic and inherently unpredictable. This study shows that it is possible to extract predictable signals from the internal variability also with the help of improved model initializations.
Extreme precipitation events in the Western Black Sea Region of Türkiye pose significant risks to life and infrastructure because of interactions among Black Sea moisture supply, atmospheric circulation, and mountainous terrain. Identifying the synoptic mechanisms responsible for these events is therefore important for disaster-risk management. This study presents a long-term (1995–2024), cluster-based synoptic classification of extreme precipitation events in this disaster-prone region using ERA5 reanalysis data. A total of 637 extreme precipitation days exceeding the 90th-percentile threshold were identified. These events were classified by applying the K-means algorithm to principal component analysis (PCA) scores. Clustering was based on dynamic variables, including mean sea-level pressure, 500-hPa geopotential height, and wind components at 850 and 500 hPa, thereby reducing the direct influence of seasonal thermodynamic variability and emphasizing the dominant circulation structures. A five-cluster solution was retained based on the combined evaluation of statistical metrics, initialization robustness, and meteorological interpretability. Precipitation intensity differed significantly among the clusters (Kruskal–Wallis H = 28.20, p < 0.001). Cluster 4, classified as the Dynamic Convective regime, produced the highest mean precipitation intensity (9.59 mm day⁻¹) and the study maximum of 37.29 mm day⁻¹, predominantly during summer and autumn. Cluster 3, representing the Thermal Convective regime, exhibited a comparable mean intensity of 9.26 mm day⁻¹ and also occurred mainly during the warm season. Its enhanced moisture availability and relatively high CAPE distinguished it from the dynamically dominated regimes, although the highest CAPE values were spatially confined rather than representative of the entire domain. Cluster 1, identified as the Northerly Orographic regime, had a mean intensity of 8.57 mm day⁻¹ and occurred primarily in autumn, when moist northerly flow from the Black Sea interacted with the coastal topography. Cluster 2 represented the Cyclonic-Frontal regime and produced the lowest mean intensity (7.83 mm day⁻¹), with events concentrated in winter and spring. In this regime, large-scale ascent and frontal forcing were more influential than convective instability. Cluster 5, classified as the Southwesterly Orographic regime, was characterized by southwesterly moisture transport and terrain-induced ascent, occurring mainly during autumn and winter. The results demonstrate that extreme precipitation in the Western Black Sea Region can arise through several distinct combinations of moisture transport, dynamic lifting, thermodynamic instability, coastal convergence, and orographic forcing. Although Cluster 4 was the most frequent and produced the most intense events, Clusters 1, 2, and 5 show that extreme precipitation can also occur under weakly unstable conditions when persistent moisture supply is combined with synoptic or topographic lifting. Considerable interannual variability was observed in the annual frequencies of all five regimes. However, none exhibited a statistically significant long-term trend during 1995–2024 (p > 0.05). These findings indicate that the occurrence frequencies of the identified synoptic regimes remained broadly stable over the study period, while their contrasting physical mechanisms should be considered separately in seasonal flood-risk assessments and in the interpretation of regional climate projections.
Nature-based solutions (NbS) are increasingly promoted as sustainable approaches for reducing drought impacts, yet evidence of their effectiveness under both current and future climate conditions remains limited. This study assessed the effectiveness of bush encroachment control in mitigating agricultural and hydrological drought severity under historical and future climate conditions (1982–2100) in the Ganale Dawa River Basin, Ethiopia. Remote sensing data, bias-corrected CMIP6 climate projections, and a Random Forest modelling framework were integrated to simulate soil moisture and runoff under SSP2-4.5 and SSP5-8.5 scenarios. Three bush encroachment management scenarios: (i) severe bush encroachment (SBE), (ii) moderate bush clearing (MBC), and (iii) intensive bush clearing (IBC) was compared with a baseline condition to evaluate their effects on drought severity. Results showed that unmanaged bush encroachment increased drought severity, whereas bush clearing interventions reduced drought severity across all climate periods. The magnitude of the response depended on management intensity and climate pathway. SBE increased agricultural drought severity by up to 21
Reliable solar resource assessment is essential for photovoltaic (PV) planning, particularly in tropical monsoon regions where seasonal cloud development causes substantial variability in surface solar radiation. Although Earth Observation (EO) datasets are widely used to estimate solar irradiance, most studies emphasize resource magnitude rather than operational reliability. This study proposes an EO-based framework for assessing solar resource reliability using Google Earth Engine (GEE). Daily all-sky Global Horizontal Irradiance (GHI), clear-sky GHI, cloud cover, and precipitation were derived from ERA5 reanalysis and GPM IMERG for Phuket Province, Thailand, during 2015–2025, yielding 4,018 daily observations. Four indicators were evaluated: cloud-induced radiation loss, the Solar Availability Index (SAI), Reliability Index (RI), and Stability Index (SI). Mean daily all-sky GHI was 5.38 ± 1.29 kWh m⁻² day⁻¹, compared with a clear-sky mean of 6.83 ± 0.47 kWh m⁻² day⁻¹, indicating that approximately 21
Understanding how rapid land-use and land-cover (LULC) changes influence landscape structure and regional climate remains a major challenge in ecologically sensitive regions such as the Western Ghats. Here, we adopt a thermodynamic entropy-based framework integrated with multi-source geospatial datasets to quantify landscape fragmentation and its climatic implications over the Idukki district, located in the foothills of the Western Ghats, a global biodiversity hotspot. Boltzmann entropy change is estimated from digital elevation data (DEM) that are 15 years apart, and a positive change in entropy implicitly suggests an enhancement in anthropogenic influence over the study region. This observation is corroborated by LULC change analysis, which revealed consistent expansion in built-up areas ( 40 km²/year) and irrigated pasture ( 200 km²/year), along with declines in fallow ( 120 km²/year) and wasteland ( 160 km²/year). These transitions in landscape have influenced the regional surface energy balance, marked by an increase (decrease) in surface temperature and sensible heat flux (soil moisture and latent heat flux). Elevation-dependent warming trends, with higher rates in low-lying regions (0.135 K/decade), align spatially with areas of elevated entropy, reinforcing the role of human-driven land degradation. These findings highlight the pressing need for climate-resilient land management in the Western Ghats and emphasize the importance of integrating high-resolution field data and land-atmosphere modeling to better understand feedbacks between landscape change and regional climate dynamics.
Following the Wenchuan earthquake, the affected areas of southwestern China experienced rapid socioeconomic recovery through a systematic urban-rural reconstruction effort. However, the post-earthquake urbanization process has put considerable strain on regional ecosystems and sustainable development. Ecological sensitivity evaluation (ESE) is a vital tool for identifying and predicting ecological constraints, providing essential guidance for ecological conservation and sustainable land use amidst rapid urbanization. In this study, Deyang City—a region severely impacted by the earthquake—was selected as the case study, and urbanization-related indicators were identified as control evaluation factors: population density, nighttime light, annual precipitation, normalized difference vegetation index (NDVI), and land use. Multi-stage data from 2010, 2015, and 2020 were used to establish a multi-phase ESE by integrating the analytic hierarchy process (AHP), the entropy index method (EIM), and logistic regression (LR), as well as an AHP EIM ensemble model. Area under the curve (AUC) was used to verify the accuracy of the various models. The results showed that ecological sensitivity in the Deyang area was relatively high from 2010 to 2020 and that it exhibited significant spatial clustering, which was strongly associated with topography, land use, and population migration. Furthermore, land use, annual NDVI, and population density were the primary drivers of ecological sensitivity evolution during urbanization. This study provides a scientific basis for the spatial planning and sustainable development of Deyang City and other rapidly urbanizing regions, as well as for reconstruction in earthquake-affected areas worldwide.
Soil moisture (SM) is a critical determinant of drought development, persistence, and severity, yet its integration into drought assessments remains limited. The Brahmaputra Valley (BV) in North-East India (NEI), despite receiving high monsoonal rainfall, has experienced recurrent droughts in recent decades, highlighting the need for integrated drought assessment frameworks. This study examines the relationship between SM and drought in the BV using a multi-index approach that combines meteorological and remote sensing-based indicators. Root-zone SM (SM100) derived from ERA5-Land (1980–2022) was analysed alongside the Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), and remote sensing-based drought indices, including the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). Seasonal spatial analysis reveals strong linkages between SM100 and remote sensing indices, with pronounced variability across all seasons. Trend analysis indicates a decline in both PDSI and SM. Change-point detection using the cumulative sum (CUSUM) method highlights hydroclimatic regime shifts: the standard CUSUM identifies breakpoints at 2011 for PDSI and 2005 for the Fraction of available water (Faw) anomaly, whereas the recursive CUSUM identifies 2011 as a shared change point in both series. Multi-Channel Singular Spectrum Analysis (MSSA) further reveals scale-dependent coherence between PDSI and Faw anomaly, with dominant shared variability concentrated at interannual timescales ( 3 years). The Faw anomaly exhibits additional low-frequency persistence, consistent with subsurface moisture memory. These findings demonstrate the value of integrating SM into drought diagnostics and support improved drought monitoring and climate resilience planning in the BV.
Air temperature forecasting plays an important role in supporting decision-making in climate-sensitive sectors such as agriculture, environmental management, and public safety. In this work, we introduce TempFusionNet-VMD, a hybrid deep learning framework that combines Variational Mode Decomposition (VMD) with TempFusionNet” (Licer et al. 2025) a multi-branch architecture integrating temporal convolutional networks (TCN), gated recurrent units (GRU), and long short-term memory (LSTM). The model uses adaptive fusion mechanisms to jointly exploit decomposed signal components and historical temperature observations, considering input windows of 5, 10, 15, and 20 days to predict future temperatures up to 14 days ahead. The proposed approach is evaluated on four Moroccan cities, namely Dakhla, Laayoune, Boujdour, and Tan-Tan, chosen to reflect different local climatic characteristics. The experimental results show that the model provides consistent improvements in forecasting accuracy across the tested configurations. Performance is assessed using standard metrics, including mean absolute error (MAE), root mean square error (RMSE), R^2 , and Accuracy of Temperature Forecasting (ATF). For short-term horizons, the model achieves RMSE values as low as 0.30, ^∘ C, along with ATF values exceeding 98. These results suggest that combining signal decomposition with hybrid deep learning architectures can be beneficial for temperature forecasting in the considered settings, and provide a basis for further investigation in broader climatic contexts.
Air pollution remains one of the most critical environmental and public health challenges worldwide, with rapidly expanding megacities such as Hyderabad experiencing substantial deterioration in air quality due to accelerated urbanization and emission growth. This study provides a comprehensive assessment of spatiotemporal particulate matter (PM2.5) variability and machine-learning-based forecasting across four contrasting urban settings (industrial, traffic-dominated, suburban, and peri-urban). The analysis was based on seven years (2018–2024) of high-resolution pollutant and meteorological data. Statistical analysis revealed pronounced diurnal bimodality, monsoon-driven reductions, and enhanced accumulation of pollutants during winter and post-monsoon periods. These patterns underscore the combined influence of emission sources, land-use characteristics, and atmospheric mixing processes. To produce operationally relevant forecasts, three advanced machine-learning models (Random Forest, RF; Artificial Neural Network, ANN; and Light Gradient Boosting Machine, LGBM) were developed and evaluated. The RF model showed the most stable and accurate performance across sites, achieving test root-mean-square error values as low as 5.96 µg m⁻³, coefficients of determination up to 0.85, and index of agreement values exceeding 0.96. ANN and LGBM also performed strongly, effectively capturing short-term pollution spikes and seasonal transitions. Feature-importance and correlation analyses identified co-emitted pollutants (carbon monoxide, CO; nitric oxide, NO; and sulfur dioxide, SO2), relative humidity, wind parameters, and boundary-layer height (BLH) as dominant predictors, highlighting the role of both emission intensity and meteorological modulation. Uncertainty analysis indicated consistently narrow prediction intervals for RF and LGBM, reinforcing their reliability across diverse atmospheric conditions. By integrating multi-site monitoring, multi-model evaluation, physically interpretable meteorological drivers, and uncertainty assessment, this study addresses the limitations of earlier single-site or single-model approaches. The resulting framework delivers high-accuracy, site-specific PM2.5 predictions. It also offers actionable insights for traffic management, industrial emission control, seasonal mitigation strategies, and public-health advisories and is readily transferable to other Indian and global megacities facing similar air-quality challenges.
The Awash basin in Ethiopia faces severe water insecurity driven by high climate variability, recurrent droughts and floods, and rising water demand—conditions projected to intensify under climate change. This study examines historical (1981–2010) and future (2020s, 2050s, 2080s) hydroclimatic changes under SSP2-4.5 and SSP5-8.5 scenarios, focusing on precipitation, temperature, streamflow, drought, and flood dynamics, with detailed analysis for the Mojo catchment. Outputs from seven top-performing CMIP6 GCMs were bias-corrected using quantile mapping for precipitation and variance scaling for temperature. The Soil and Water Assessment Tool Plus (SWAT+) hydrological model is used to simulate streamflow across Mojo catchment in Awash basin. Results reveal strong spatial contrasts in precipitation trends. During March–May (MAM), the Upper Awash basin shows minimal PRCPTOT change (< 2 mm), slight increases in CDD, and declines in wet-day frequency and extreme rainfall. The Middle Awash basin exhibits reduced PRCPTOT and CWD but higher CDD (+ 1–4 days), while the Lower Awash basin shows marked increases in CDD and 2023decreases in CWD. In June–September (JAS), PRCPTOT rises in the Upper basin (+ 12 mm) but declines in the Middle (− 24 mm) and Lower basins. Future projections indicate increased MAM rainfall in the Upper and Lower basins (up to + 50
Global warming is reshaping human thermal exposure by increasing potential cooling requirements and reducing heating requirements, yet the combined effects of climate change, demographic dynamics, and socioeconomic inequality remain poorly quantified across the globe. Here, we integrate bias-corrected projections from five global climate models with high-resolution gridded population data to evaluate global population exposure to cooling degree days (CDD) and heating degree days (HDD) across different Shared Socioeconomic Pathways (SSPs) over the 21st century. We further investigate disparities in thermal across baseline income cohorts and decompose the corresponding changes into population-driven, climate-driven, and composite effects. Our results indicate that CDD exposure increases across all scenarios, with the highest growth rates found in densely populated tropical and subtropical regions. Disproportionate growth is observed in low- and lower-middle-income countries, which implies expanding inequality in adaptation challenges ahead. In contrast, HDD exposure exhibits widespread declines in mid- and high-latitudes. However, heterogeneous responses are observed among individual countries, where population growth may partly mitigate the reduction of HDD. Decomposition analysis shows that population effects strongly amplify CDD exposure in many rapidly growing low-latitude countries, while climate effects play an important role in HDD changes across many higher-latitude countries. Sensitivity analyses further indicate that the relative contributions of population and climate effects vary across different scenarios. The results reveal an increasingly asymmetric global distribution of thermal exposure, highlighting the need for adaptation strategies that integrating climate mitigation, demographic dynamics, energy system planning, and equity considerations.
Using high-resolution ENACTS rainfall, ERA5 wind and specific humidity at different pressure levels, and K-means clustering, this study demonstrates that Ethiopia’s summer (Kiremt; June–September) monsoon rainfall is significantly regulated by the intensity and latitudinal shifts of the Tropical Easterly Jet (TEJ at 200 hPa) and the African Easterly Jet (AEJ at 600 hPa). Key quantitative results reveal that a stronger TEJ, which reaches its maximum intensity (> 25 m s⁻¹) and northernmost latitude (12.5°–13°N) in July, strongly enhances rainfall across all regimes through a significant negative correlation (r = − 0.4 to − 0.6) with upper-level easterlies, peaking in August and delaying seasonal drying into September. Conversely, the mid-level AEJ peaks in August–September and displays a positive correlation (r = + 0.4 to + 0.6) between wind intensity and rainfall suppression by limiting inland moisture transport and accelerating monsoon withdrawal. However, extreme northward displacements of the AEJ (14°–16°N) during wet months instead promote widespread positive rainfall anomalies (r ≈ + 0.4) by enhancing mid-level moisture transport and vertical wind shear. Composite diagnostics confirm that these wet months are dynamically characterized by upper-level divergence, mid-level ascent, and convergent southwesterly moisture fluxes. In contrast, dry periods are associated with weakened TEJ and southward shifts of the AEJ, which induce atmospheric subsidence and moisture divergence. Ultimately, these findings a critical need for next-generation numerical weather prediction (NWP) and artificial intelligence (AI) weather and climate models to explicitly resolve and assimilate the dynamic impacts and spatial shifts of the AEJ and TEJ to significantly improve regional predictability.
Methane is the second most potent anthropogenic greenhouse gas, making the accurate quantification of its atmospheric sink vital for regional climate modeling. This study presents a high-resolution, time-dependent computational analysis of the primary CH4 chemical sink (reaction with the hydroxyl radical, •OH) over the Iranian plateau and surrounding territories from March 2024 to February 2025. Using CAMS and ERA5 reanalysis, we estimate a mean annual CH4 loss rate of 1.5502× 10^5 molecule⋅cm− 3⋅s− 1 across Iran. The highest loss rates cluster heavily along the humid coastlines of the Caspian Sea and the Persian Gulf. Temporally, the sink exhibits a strong seasonal cycle, but the maximum activity occurs during spring and autumn (specifically November and March) rather than the hottest summer months. This counterintuitive pattern indicates that relative humidity heavily governs the available •OH concentration, which ultimately overrides the expected Arrhenius temperature dependence. These localized, highly resolved estimates offer a baseline for refining national methane budgets and improving regional climate forecasts.
