Environmental and Earth Sciences

Abstract: El Niño–Southern Oscillation (ENSO) modulates tropical South American rainfall, but its Colombian expression is filtered by terrain, rainfall regime, moisture pathways, and atmospheric state. We quantify ENSO-related sensitivity of standardized precipitation anomalies in the Colombian Andes and Orinoquia using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0; 1981–February 2026), station records from Colombia’s Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), ERA5 predictors, and 1981–2010 climatologies. CHIRPS reproduced station-derived standardized anomalies (r=0.94 in the Andes; r=0.91 in Orinoquia), supporting regional anomaly analysis while retaining cautious comparison framing. Lagged associations with the Oceanic Niño Index (ONI) were evaluated for lags 0–6 months using effective degrees of freedom, block-bootstrap confidence intervals, and maximum-lag tests. ENSO sensitivity was stronger and more coherent in the Andes: annual lag-1 ONI–precipitation correlation was −0.374, with marked December–February and June–August responses. Orinoquia showed weaker annual sensitivity and a season-specific September–November response. Diagnostic models showed modest, temporally cautious skill gains from physically interpretable climate-index and reanalysis predictors in the Andes. ENSO-related Colombian precipitation sensitivity is heterogeneous, lagged, and physically mediated rather than spatially uniform.

Abstract: Estimating PM₂.₅ exposure in high-altitude Andean cities is challenging due to limited monitoring coverage and the complex interactions among topography, meteorology, and atmospheric chemistry. This study presents a comparative assessment of Random Forest and Extreme Gradient Boosting (XGBoost) for urban-scale PM₂.₅ estimation in Quito, Ecuador. Ground-based observations from the Metropolitan Atmospheric Mon-itoring Network of Quito (REMMAQ) were integrated with Sentinel-5P TROPOMI products, meteorological variables, and topographic predictors processed in Google Earth Engine. Models were developed separately for wet and dry seasons to account for seasonal variability. XGBoost achieved the highest predictive accuracy during the wet season (R² = 0.73), when topographic controls dominated PM₂.₅ variability and the DEM emerged as the most influential predictor. In contrast, RF demonstrated greater robustness during the dry season (R² = 0.63), when photochemical interactions became increasingly important and the CO–SO₂ combustion index was the dominant predictor. Spatial predictions identified a persistent north–south pollution corridor within the urban core of Quito. These findings indicate that PM₂.₅ dynamics in inter-Andean val-leys are governed by seasonally shifting physical and chemical controls. The proposed framework provides a scalable approach for generating spatially continuous air-quality estimates in mountainous urban environments with limited monitoring in-frastructure.

Abstract: Hydrometeorological extremes in oceanic island environments are influenced not only by rainfall intensity but also by wet-condition persistence and the interaction of multi-ple atmospheric drivers. This study investigates extreme, persistent, and compound hydrometeorological events at the Chã de Macela station (São Miguel Island, Azores) using a quality-controlled daily dataset for 2011–2023. Compound events were defined as days simultaneously exceeding the precipitation P95 threshold (95 th percentile), wind-speed P90 threshold (90 th percentile), and relative-humidity P90 threshold. Eight compound events were identified. Results revealed a strong relationship between persistence and hydrometeorological severity, with 75% of compound events and nearly half of all P99 (99 th percentile) precipitation events occurring within persistent wet spells. This indicates that the most severe hydrometeorological conditions gener-ally develop within pre-existing humid regimes rather than as isolated rainfall epi-sodes. ERA5 reanalysis data were used to analyse mean sea level pressure, total column water vapour, and synoptic circulation patterns. Extreme precipitation events were associ-ated with significantly lower pressure and higher atmospheric moisture than ordinary conditions (Mann-Whitney test, p < 0.001). Synoptic analysis revealed multiple at-mospheric pathways, including cyclonic systems, strong meridional pressure gradi-ents, and developing large-scale disturbances. These findings demonstrate that com-pound hydrometeorological extremes in the Azores arise through different synoptic mechanisms but are consistently linked to strong atmospheric forcing over the North Atlantic.

Abstract: Artificial intelligence weather models achieve forecast skill comparable to numerical weather prediction at far lower computational cost, yet their reliability for high-impact extremes remains largely uncharacterized. We evaluate Aurora, a state-of-the-art deterministic AI model, using an event-based framework spanning tropical cyclones, freezes, heatwaves, atmospheric rivers, and extreme precipitation at lead times from 1 to 21 days. Aurora demonstrates strong short-range (1–7 day) skill: mean tropical cyclone track errors of 20–60 km at 1–3 day leads, high spatial agreement for temperature extremes (IoU ≥ 0.78), and accurate atmospheric river structure reproduction. Beyond 7–10 days, amplitude collapses as surface fields regress toward climatology, consistent with theoretical Lorenz predictability limits, while large-scale circulation patterns remain moderately skillful (pattern correlations 0.57–0.85 at 14–21 days for temperature extremes). This pattern–amplitude divergence, where synoptic-scale structure persists but threshold-based extremes collapse, is the central finding; event-specific failures include catastrophic TC recurvature errors, systematic intensity underestimation, and pronounced in-sample versus out-of-sample precipitation skill degradation. Aurora provides reliable deterministic guidance within 7–10 days, positioning it as a computational anchor for hybrid probabilistic forecasting systems rather than a standalone operational replacement.

Abstract: Alternating marine inflow and continental outflow make southern Sweden a suitable region for investigating natural and anthropogenic contributions to background particulate matter (PM). However, the interpretation of dust and marine aerosol sources remains challenging because their source signatures often overlap during atmospheric transport. This study investigates aerosol sources at the Vavihill and Hyltemossa background stations using source apportionment, elemental analysis, transport modelling, reanalysis data, and particle resolved microscopy. At Vavihill, size segregated filter samples were used to determine PM₁₀ and PM_2.5 mass concentrations and elemental composition, followed by Positive Matrix Factorization (PMF). At Hyltemossa, online XACT elemental measurements were combined with FIDAS coarse PM observations to evaluate coarse mode source contributions. HYSPLIT backward trajectories, CAMS diagnostics, and SEM/EDX analysis supported the interpretation of selected dust related episodes and particle mixing state. The results show that background PM in southern Sweden is influenced by mineral and marine related aerosols, local pollution, and mixed combustion particles. Observations from Vavihill estimated that mineral related aerosols increased significantly during spring and contributed up to approximately 38% of total PM₁₀. Studies combining source apportionment with trajectory analysis, CAMS diagnostics, and SEM/EDX can strengthen the interpretation of background aerosol sources and particle mixing conditions in areas affected by marine and continental air masses.

Abstract: Regional climate models (RCMs) have become established as essential dynamical downscaling tools, providing physically consistent, high-resolution climate information where global climate models (GCMs) are unable to resolve. Added value is obtained over heterogeneous vegetation, coastal, urban or orographically complex zones. Temperature, precipitation or wind benefit whenever fine-scale processes govern the climate signal, as with extreme events. CORDEX initiative (since 2009) has further consolidated RCM research with coordinated multi-model ensembles covering all continental regions, enabling systematic uncertainty quantification of regional climate projections. PROMES RCM has been an active scientific contributor across three decades to the modelling community ensemble. This review synthesizes and documents PROMES’s development, performance across Europe, West Africa and South America, and assesses its scientific contributions. This includes climate change projections features, extreme events (droughts, heat waves, or Mediterranean tropical-like cyclones) or land surface–atmosphere studies. Its behaviour within the more relevant international multi-model ensembles (from PRUDENCE to EuroCORDEX), with structurally independent characteristics, represents a scientific asset for uncertainty characterization beyond the model’s individual results, offering a legacy argument for preserving RCM diversity in ensemble design strategies, as it is an essential and many times underappreciated key point of uncertainty analysis.

Abstract: Catalonia has a Mediterranean climate with intense, long drought or rainy periods, which are difficult to manage. The variable topographic and sea conditions also contribute to modulating the extreme atmospheric regime. The aim of this research is to model the precipitation regime of the region based on different radar and lightning fields. To conduct the analysis, several points have been selected with different heights to evaluate the monthly values and establish the common yearly patterns. It has been observed that there are two principal behaviours: single and double maxima modes. Single peak usually occurs during warm months (mainly July and August), meanwhile the bimodal maxima are concentrated on Spring (March) and Autumn (October or November).

Abstract: Brick manufacturing is among the oldest industries that play a crucial role in socio-economic development, particularly in developing countries i.e. Pakistan. Pakistan is the 3rd largest brick producer in the world with 70 billion annual brick production and 20,000 kilns, after China and India. In Pakistan, a high percentage of kilns are more than a century old while a fraction of kilns shifted to zig-zag technology and the remaining kilns are working on the older technologies like Fixed Chimney Bull's Trench Kiln (FCBTK). Additionally, low-quality fuels are used, and no safety measures are there. Due to such reasons, brick kilns significantly impact air quality and health in Pakistan, which is already experiencing severe air quality problems. Brick kilns emit a huge number of pollutants into the environment including sulfur dioxide, carbon oxides (CO and CO₂), nitrogen oxides, particulate matter, carcinogenic dioxins, fluoride compounds, H₂S (hydrogen sulfide), polycyclic aromatic hydrocarbons (PAHs) and carbon black etc. Direct inhalation of pollutants causes respiratory diseases, nervous system diseases, cardiovascular diseases, cancer, skin diseases, and reproduction problems. Workers and nearby communities suffer more than others. These emissions also pose indirect impacts by polluting the environment, ozone depletion, acid rain, smog, global warming, and climate change. We need to shift to cleaner production and the modern technologies of brick manufacturing to overcome environmental and health challenges. For example, we can use fly ash, waste glass powder and plastic to produce our bricks rather than using clay. It not only cuts down emissions but also will be helpful to cope with health challenges.

Abstract: Meteorological drought is a major hydroclimatic hazard across South Asia, with far-reaching consequences for water security, agriculture, ecosystems, and cli-mate-resilient development. In India, where hydroclimatic variability is strongly gov-erned by the Indian summer monsoon, understanding how future precipitation re-gimes and drought-event characteristics may evolve under anthropogenic forcing is essential for effective adaptation planning. This study examines projected changes in monsoon precipitation and meteorological drought characteristics across India’s six homogeneous precipitation zones using bias-corrected NASA NEX-GDDP-CMIP6 daily precipitation simulations from 19 global climate models. Model performance is evaluated against high-resolution gridded precipitation observations from the India Meteorological Department for the historical reference period 1985–2014. Future changes are assessed for three 21st-century periods: near future (2021–2050), mid fu-ture (2051–2080), and far future (2081–2100), under SSP2-4.5 and SSP5-8.5. The ability of the multi-model ensemble to reproduce observed monsoon rainfall patterns is quantified using the Pattern Correlation Coefficient and Root Mean Square Error. Me-teorological drought characteristics, including severity, duration, and intensity, are derived from the 12-month Standardized Precipitation Index using a run-theory-based event framework. The findings show that the CMIP6 multi-model ensemble reasonably captures the broad spatial organization of Indian summer monsoon rainfall, although precipitation magnitudes are generally underestimated relative to observations. Future projections indicate an overall intensification of mon-soon precipitation, particularly under SSP5-8.5, with far-future increases exceeding 30–40% over parts of central and western India. However, this projected wetting ten-dency does not imply a uniform reduction in drought risk. Instead, SPI-12-based drought diagnostics reveal pronounced spatial and temporal heterogeneity in drought-event behaviour, with enhanced severity, persistence, and intensity emerging most prominently over Central North-East, North-West, and West-Central zones. The results highlight a critical hydroclimatic paradox: increasing mean precipitation can coexist with intensified meteorological drought when rainfall variability, seasonal re-distribution, and dry-spell persistence increase. Overall, the study demonstrates the value of bias-corrected ensemble projections and event-based drought diagnostics for identifying regional drought vulnerabilities and supporting climate adaptation, drought preparedness, and water-resource planning across India.

Abstract: The Aura Microwave Limb Sounder (Aura/MLS) measures temperature profiles with a horizontal spacing of about 170 km along its near polar orbit. We highpass-filtered the horizontal temperature fluctuations along the suborbital track in the middle atmosphere. The characteristics of inertia-gravity waves with horizontal wavelengths between 200 and 825 km are evaluated for the equatorial region (10°S to 10°N), northern polar region (70°N to 82°N), and southern polar region (70°S to 82°S) over the time interval from August 2004 to December 2021. The gravity wave activity over the southern polar region is stronger by a factor of up to 2 than over the northern polar region. The seasonal variation of the vertical structure of gravity wave activity shows strong interhemispheric differences. There are double layers of enhanced gravity wave activity in the upper mesosphere over Antarctica in summer and winter while the northern polar region does not show a double layer structure of gravity wave activity. In the northern polar region, the upper mesospheric gravity wave activity is decreased after the onset of major sudden stratospheric warmings.

Abstract:

The effects of soil drought on evapotranspiration (ET) and canopy conductance (Gc) are extensively investigated in forests, but the responses of ET and Gc to atmospheric drought and compound drought still remain unclear in the plantations. Environmental factors and ET were continuously measured in a Pinus sylvestris var. mongolica plantation located in the semi-arid areas of Northeast China during the growing seasons (May–September) in 2020–2024. Compared with non-drought, ET increased by 34.96% under atmospheric drought, and decreased by 23.58% and 28.86% under soil drought and combined drought, respectively. Compared with non-drought, Gc decreased by 29.27%, 15.19%, and 68.74% under atmospheric drought, soil drought, and combined drought, respectively. Under non-drought, atmospheric drought, and soil drought, ET was mainly controlled by net radiation (Rn) with a relative contribution of 41.78%, 44.67%, and 30.88%, respectively. Under combined drought, the dominant factor influencing ET was relative extractable water (REW) with a relative contribution of 47.97%. Under non-drought, the dominant controlling factor of Gc was vapor pressure deficit (VPD), followed by Rn. Under atmospheric drought and soil drought, the sensitivity of Gc to VPD was much higher than other environmental factors. Under combined drought, the most important controlling factor of Gc was REW, followed by VPD. This study proved that different drought types have different effects on ET and Gc. Under warmer and drier climates, the management practices should be used to cope with the increasing water stress to ensure the sustainable development of the Pinus sylvestris var. mongolica plantation in semi-arid areas of Northeast China.

Abstract: Elastic backscatter lidar and ceilometer systems provide continuous observations of aerosol and cloud vertical structure, but the interpretation of conventional attenuated backscatter products is often limited by the dominance of signal amplitude, strong event-to-event variability, and the reduced visibility of subtle internal features. In this study, we present a refinement framework designed to extract additional structural information from elastic lidar measurements through multiscale local diagnostics applied directly to the native backscatter field. The methodology combines standardized residual fields, local gradients, variance-based metrics, space-time decorrelation scales and structure functions to highlight atmospheric boundaries, internal layering, mixing zones, and coherent structures that are not always evident in conventional representations. The approach is evaluated through three contrasting atmospheric case studies observed in 2024. Two spring events are asso- ciated with mineral dust intrusions characterized by different vertical coupling with the planetary boundary layer, while a summer case represents a non-dust regime dominated by diurnal boundary-layer evolution. The refined diagnostics consistently reveal features hidden or only weakly visible in the raw backscatter field, including sharp interfaces, embedded stratification, wave-like perturbations and transitions between decoupled and mixed atmospheric states. Results show that the proposed metrics enable a more objective description of aerosol-layer dynamics and boundary-layer interactions without requiring complex inversion procedures or auxiliary measurements. Since the method relies only on standard elastic lidar observations, it is in principle readily transferable to ceilometer and lidar monitoring networks. The framework therefore offers a practical pathway for enhanced atmospheric feature detection, improved interpretation of routine profiling ob- servations, and future automated classification of aerosol and boundary-layer regimes.

Abstract: This study introduces a novel empirical model to estimate the total pressure drag generated by trapped lee waves and upward-propagating internal waves in moderate to strong non-hydrostatic, stratified flow over a mountain ridge, as a function of flow nonlinearity. The core framework is based on a two-layer atmosphere characterised by a piecewise-constant Scorer parameter, l, where a lower layer of constant l1 underlies an upper layer with l2&lt;l1. This framework incorporates key features to extend beyond idealized assumptions, providing a reliable predictive tool for predicting non-linear flow regimes over mountainous terrain, particularly those featuring realistic vertical profiles of the Scorer parameter. To develop the empirical formulation, a micro- to mesoscale numerical model is employed to simulate realistic, nonlinear flows over steep topography. The proposed empirical model yields results that compare favourably with numerical simulations across a range of moderate to strong non-hydrostatic regimes, including complex cases derived from observational data and realistic vertical profiles of the Scorer parameter. Ultimately, this empirical approach serves as a valuable foundation for improving drag parameterisations in weather prediction models, offering a computationally efficient alternative to high-resolution numerical downscaling over steep terrain.

Abstract: Marine low clouds (MLCs) strongly affect Earth’s radiation budget due to their extensive coverage and strong reflection of incoming solar radiation. Despite their important role in the Earth system, the extent and mechanisms of MLC response to climate oscillations are not well understood. In this study, the effect of the El Niño–Southern Oscillation (ENSO) on cloud and meteorological properties across the Pacific Ocean is investigated by integrating various satellite observations and reanalysis datasets. The results reveal a pronounced hemispheric asymmetry in the response of subtropical MLCs to ENSO. During El Niño events, the Northeast Pacific exhibits reduced cloud cover and weaker shortwave radiative cooling, while an opposite response is observed over the Southeast Pacific, where cloudiness and radiative cooling are enhanced. These contrasting responses are linked to distinct ENSO-driven meteorological changes between the two hemispheres. Over the Northeast Pacific, El Niño conditions weaken inversion strength and the subtropical high, suppressing MLCs. In contrast, the Southeast Pacific experiences enhanced inversion strength and lower-tropospheric geopotential height during El Niño, which favor MLC development. It is suggested that hemispheric asymmetries in the climatological positions and ENSO-induced responses of the Pacific subtropical highs contribute to the opposite MLC responses between the two hemispheres. These findings highlight the importance of large-scale controls in shaping regional cloud responses to climate variability and provide insights for improving cloud representation in global climate models.

Abstract: Agrivoltaics (APV) systems, integrating solar energy generation with agriculture, offer a promising solution for optimizing land use facing a rising energy demand and climate change concerns. However, the impact of APV induced shading on orchards micrometeorology and physiology is not fully understood. This study investigated the effects of simulated APV shading on sensible heat flux, temperature, humidity, wind, CO₂ flux, and evapotranspiration (ET) in deciduous plum and nectarine orchards in northern Israel. Using the eddy covariance (EC) method, we measured CO₂ ​ and water vapor fluxes in adjacent shaded and unshaded (referred to as ‘paneled’ and ‘sunlit’) sections. Principal component analysis (PCA) and linear regression were employed to analyze the relationships between meteorological variables and the measured fluxes. Results showed significantly higher rates of CO₂ ​ flux (absorption) and ET in the paneled sections compared to sunlit sections, particularly during summer peak radiation hours. These findings suggest that partial shading moderates environmental stress (excessive heat, high vapor pressure deficit), improving stomatal function, enhancing photosynthesis, and potentially promoting water use efficiency. This research integrates the EC method with APV system analyses in orchards, providing novel insights into the dynamic interactions under shading and highlighting the potential of APV to enhance agricultural sustainability in semi-arid climates.

Abstract: Accurate information on atmospheric temperature profiles is crucial for improving numerical weather forecasting and short-term numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which fails to meet the high spatial resolution required for NWP. In this study, we present a method to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) using data from the polar-orbiting satellite FY-3C. The aim is to provide high-resolution atmospheric structure for NWP in the TP. The temperature profile in VASS exhibits temporal and spatial heterogeneity due to the significant impact of clouds on the radiative transfer mode (RTM). To address this, we employ a combination of variation and artificial neural network (Var-ANN) methods to calibrate the satellite product and improve its compatibility with the model. To confirm the feasibility of our method, we compare the calibrated results with the observed data from 121 radiosonde soundings and 2400 meteorological stations in China, both of which represent conditions closest to the real atmospheric states. The calibrated temperature shows improvements over the original temperature, with a root mean square error, bias, and agreement with radiosonde soundings of 2.11, -0.72, and 0.998, respectively. We also select two classical cases involving the eastward movement of the plateau vortex (PV) and the formation of precipitation to verify the applicability of the calibration in NWP. The results demonstrate that the performance of NWP improves after assimilating the calibrated data, with the Var-ANN data assimilation scheme achieving the highest threat score of 66.9 and 66.7 for case 1 and case 2, respectively. These findings suggest that the Var-ANN method is suitable for calibrating satellite temperature profiles, and the calibrated data holds potential for precipitation forecasting. Furthermore, the novel method can also be applied in global temperature profile correction and satellite cross-calibration.

Abstract: Recent studies have investigated whether the rate of global warming has changed since the 1970s, with particular attention to the role of natural variability and its removal from temperature time series. In particular, Foster and Rahmstorf (2026) analyzed global mean surface temperature series, adjusted for natural variability. However, their procedure might produce spurious changepoints, since it does not appropriately handle the autocorrelation present in the residuals of the models considered. In this study, we revisit the same adjusted temperature series using a different methodology (the Quandt likelihood ratio test) while properly accounting for the presence of autocorrelation. We find evidence that global temperature has departed from its previous path since around 2013-2014. Our results provide a robust proof of a clear recent increase in the temperature trend, at a rate of warming that has doubled since that date.

Abstract: We propose a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) for the simultaneous bias correction of five hydroclimatic variables derived from the NASA POWER satellite product: minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (HR), solar radiation (Rad), and precipitation occurrence (Pbin). The GMFAMM extends the univariate functional framework by incorporating a shared latent Gaussian process Λ0i(t) that captures cross-variable thermodynamic dependence. A systematic experimental grid of more than 200 model configurations across four distributional families (Gaussian, Gamma, Poisson, Binomial), two effect structures (linear and smooth P-splines), and four nested covariate sets is evaluated on a strict chronological 70/30 hold-out – seven training years (2016–2022) and three hold-out years (2023–2025) – to identify the optimal marginal specification for each variable. The value of joint modelling is quantified through a two-stage cross-residual approximation to the GMFAMM shared latent process, which constitutes a conservative lower bound on the gains achievable by the full simultaneous model: out-of-sample RMSE is reduced by 53% for Tmin, 38% for Tmax, and 51% for relative humidity relative to the independent GAMM baseline. These gains are physically interpretable through the Clausius-Clapeyron thermodynamic coupling documented in the residual cross-correlation analysis. The trained model artefacts are deployed in ColClim, an open-access R Shiny web application that queries the NASA POWER API and the Open-Meteo forecast service for any user-selected location in Colombia, applies the GMFAMM correction pipeline, and delivers both historical bias-corrected time series and short-range (1–16 day) forecasts across the five variables.

Abstract:

It is known that snowcover properties change rapidly due to effect of weather and radiation, detailed models mapping effect of weather and radiation processes to evolution of snowpack have been developed. These models are capable of accurately simulating entire evolution of snowpack at a specific point if a sufficiently detailed time-series of weather and radiation parameters affecting the point is known. In this study we consider the reverse problem of finding the weather and radiation parameters that lead to changes in snowpack parameters, we have used a simulation approach to study the feasibility of finding this reverse map. We mapped a time-series of snowcover states to their corresponding time-series of weather and radiation states using a machine learning model. The data of snowcover states was generated using a well known and rigorously validated snowcover simulation model (SNOWPACK). The results of our experiments show that snow surface time-series contains important information about the meteorological time-series affecting it. We were able to find the meteorological parameters from the simulated data under certain conditions, we expect these results to generalize with actual data. There maybe important applications of these results in optimization of weather data collection systems, weather interpolation algorithms and downscaling algorithms, combining the snowpack data with weather observations can lead to improvements in these algorithms. This study makes a preliminary feasibility study of the reverse problem, our results are positive and encourage further field work using actual data.

Abstract: This research examines how climate change intensifies urban heat stress, particularly in public spaces where mechanical cooling is impractical. A climate-driven, systems-level numerical model is developed to evaluate the pre-installation feasibility of portable, solar-powered misting canopies. Hourly Typical Meteorological Year data (TMYx, 2009–2023) are analyzed for each city to estimate photovoltaic (PV) energy yield, electrical load, potential misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. Misting operation is governed by an adaptive, rule-based control strategy based on air temperature, relative humidity, and solar radiation. To enable systematic comparison, K-means clustering is applied to classify the cities into six archetypal summer climate zones. Results indicate that evaporative cooling feasibility is driven more by ambient humidity than by air temperature. Hot-dry interior cities achieve the longest average misting duration (502.65 hours) and highest water consumption (30,486 L per module), but exhibit the lowest PV-to-load autonomy ratio (1.53) due to high energy demand for pumping. In contrast, humid Black Sea cities show minimal misting duration (13.11 hours) and water use (478 L) yet achieve the highest autonomy (40.91) because of limited system operation. It is important to note that the autonomy ratio reflects a seasonal energy balance rather than continuous off-grid capability. Overall, the adaptive control approach effectively aligns water and energy use with climatic suitability across all clusters. The proposed framework offers a scalable and quantitative screening tool to inform the design and deployment of PV-powered outdoor cooling systems across diverse urban environments.