Atmospheric Research最新文献

{"title":"Influence of sea surface temperature in the southern Indian and Atlantic Oceans on austral summer rainfall in southern Tanzania","authors":"Dickson Mbigi,&nbsp;Zacharia Florence Mtewele","doi":"10.1016/j.atmosres.2026.108847","DOIUrl":"10.1016/j.atmosres.2026.108847","url":null,"abstract":"<div><div>As one of the prominent oceanic regions exhibiting high interannual variability of sea surface temperature (SST), the southern Indian and Atlantic Oceans have been found to exert strong impacts on January–February-March (JFM) rainfall in southern Tanzania. The JFM rainfall is found to be significantly linked to the subtropical Indian Ocean Dipole (SIOD)-like SST pattern over the southern Indian Ocean and southern Atlantic Ocean Tripole (SAOT)-like SST structure over the southern Atlantic Ocean. This relationship is linearly independent of the effects of El Niño–Southern Oscillation and the Indian Ocean dipole. The presence of a weakened Mascarene high associated with the negative SIOD phase leads to southerly wind anomalies across the Mozambique Channel, which enhances moisture transport into southern Tanzania and rainfall over the region. The SAOT, on the other hand, excites a zonal elongated wave train-like pattern that induces large-scale cyclonic circulation over the southern Indian Ocean. Subsequently, the western flank of the cyclonic circulation excites southerly wind anomalies traversing through the Mozambique Channel towards the study region, leading to rainfall over the study region. Moreover, the observed wave train-like pattern is echoed in the upper levels, but the anticyclonic center over southern Australia expands further towards northern Madagascar. At this position, the associated anticyclonic wind anomalies reach southern Tanzania to create a divergence condition, enhancing rainfall conditions. Finally, the joint SIOD and SAOT indices show enhanced contribution to the rainfall, explaining about 28% of rainfall variability.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108847"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global assimilation of NRL MODIS aerosol optical thickness and its impact on aerosol direct radiative effect over a full year","authors":"Min Zhao ,&nbsp;Tie Dai ,&nbsp;Yueming Cheng ,&nbsp;Daisuke Goto ,&nbsp;Keiya Yumimoto ,&nbsp;Guangyu Shi","doi":"10.1016/j.atmosres.2026.108819","DOIUrl":"10.1016/j.atmosres.2026.108819","url":null,"abstract":"<div><div>This study investigates the accuracy of aerosol optical thickness (AOT) forecasts and analyses during a whole year, by assimilating AOT retrievals from the Naval Research Laboratory (NRL) Moderate Resolution Imaging Spectroradiometer (MODIS) into the aerosol-coupled Non-hydrostatic ICosahedral Atmospheric Model. We explore the impact of data assimilation on aerosol direct radiative effect (DRE), taking into account the interactions between aerosol particles and radiation. Evaluation against the assimilated MODIS AOT data shows an improvement in the AOT fields. The root mean square error (RMSE) dropped from 0.027 in the free-run to 0.018 (a 33% reduction) for the forecast and to 0.017 (a 37% reduction) for the analysis, while the correlation coefficient rose from 0.640 (free-run) to 0.911 (forecast) and 0.986 (analysis), respectively. Furthermore, the most significant improvements were observed during the peak biomass burning period from August to October. This enhanced performance is further certified independently by Aerosol Robotic Network (AERONET) observations, which show a reduction in RMSE from 0.050 (free-run) to 0.038 (forecast) and 0.040 (analysis), alongside a marked rise in correlation coefficient to 0.810 and 0.884, respectively. The forecast DRE under clear-sky condition at the TOA is −2.69 ± 2.02 W/m² and at the surface is −4.04 ± 2.96 W/m². Under all-sky conditions, aerosol DRE are influenced by clouds, the forecast DRE under all-sky condition at the TOA is −1.45 ± 1.26 W/m² and at the surface is −2.74 ± 1.98 W/m².</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108819"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the inversion method of vertical airflow motion in complex phase layered clouds","authors":"Yun Yuan,&nbsp;Huige Di,&nbsp;Jiale Wang,&nbsp;Dengxin Hua","doi":"10.1016/j.atmosres.2026.108843","DOIUrl":"10.1016/j.atmosres.2026.108843","url":null,"abstract":"<div><div>The vertical airflow velocity directly governs the growth and development of clouds. Accurately retrieving this velocity represents one of the pivotal and challenging aspects in cloud dynamics research. Unfortunately, no existing method can precisely retrieve the continuous vertical airflow velocity within clouds. To tackle this problem, this study assesses the feasibility of employing particles at the left end of the power spectrum as tracer particles by incorporating the intensity and critical threshold of tracer spectral points. Through an analysis of the impacts of turbulence, wind shear, and beam width on particles in various phases, the spectral width threshold is further established. Ultimately, the applicable conditions for the small particle tracing method are clearly defined. Additionally, based on power spectrum skewness and temperature, a method for retrieving vertical airflow velocity through spectral separation in a bimodal structure is proposed. The combination of these two methods can overcome the limitations inherent in single retrieval method and yield the vertical airflow velocity within the cloud's vertical structure under specific conditions.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108843"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of cloud liquid water advection in shaping sea fog over the Yellow Sea","authors":"Hao Shi ,&nbsp;Shanhong Gao","doi":"10.1016/j.atmosres.2026.108840","DOIUrl":"10.1016/j.atmosres.2026.108840","url":null,"abstract":"<div><div>Sea fog is a hazardous weather phenomenon that poses significant risks to maritime operations. To describe its dynamic morphological evolution, this study introduces the concept of <em>fog shaping</em>—a process governed by cloud liquid water (CLW) advection coupled with thermodynamic transport. Using both idealized and real-case numerical simulations, we examine the role of CLW advection in driving fog shaping over the Yellow Sea. A key finding in idealized simulations is that CLW advection contributes 61%–93% of the total effect under horizontally uniform sea surface temperature, strongly promoting downwind fog propagation. This process is further enhanced by cold-air advection to accelerate spatial expansion. Based on these results, we propose a conceptual framework in which fog shaping is governed by the balance between advective transport (CLW/cold-air advection) and local thermodynamic processes (turbulent mixing, radiative cooling, microphysical phase transitions). The framework distinguishes three shaping patterns: rapid downwind expansion (Type-A), quasi-translational motion (Type-B), and bidirectional development (Type-C). Three-dimensional simulations of two real events validate the framework. The 25 March 2018 case shows rapid northern expansion (Type-A) driven by dominant CLW advection. For the 10 May 2018 case, one experiment reveals that quasi-translational motion (Type-B) resulting from a quasi-equilibrium between advective removal and local CLW generation, while another exhibits bidirectional development (Type-C) due to a surplus of local upwind generation combined with dominant downwind CLW advection. These results underscore the critical role of CLW advection in fog shaping.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108840"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable deep learning method integrating spatial self-attention for generating bias-corrected high-resolution GFS precipitation forecasts","authors":"Haiyang Wang ,&nbsp;Shufeng Lai ,&nbsp;Chongxun Mo ,&nbsp;Tao Feng ,&nbsp;Changhao Jiang ,&nbsp;Na Li","doi":"10.1016/j.atmosres.2026.108832","DOIUrl":"10.1016/j.atmosres.2026.108832","url":null,"abstract":"<div><div>Numerical weather prediction (NWP) models are subject to inherent limitations such as insufficient resolution and systematic biases, which present formidable challenges for regional precipitation forecasting. High-precision precipitation forecasting is crucial for regional flood prevention and urban flood risk reduction. This study proposes an explainable deep learning framework (DualTransBU-Net-P), integrating spatial self-attention. This framework incorporates a core downscaling-bias correction model (DualTransBU-Net), a post-processing optimization module for extreme precipitation, and SHAP (Shapley Additive Explanations) for interpretability. It performs end-to-end joint downscaling and bias correction on Global Forecast System (GFS) precipitation forecast data by integrating multi-source data. The results show that compared to existing models, the proposed architecture significantly enhances GFS precipitation forecast accuracy, improving resolution from 0.25° to 0.025°. The root mean square error (RMSE) of the test set is reduced by 4.6% to 18.7%, and the fair threat score (ETS) is improved by an average of 43.9%. Among 437 heavy-precipitation day samples, RMSE decreased for 412 samples (94.3%). The ETS under the extreme precipitation threshold (&gt;10 mm d⁻¹) increased by 50.6% to 63.1%. Furthermore, the model's performance remained high during the seasonal analysis, demonstrating strong seasonal generalization. Interpretability analysis revealed distinct decision-making mechanisms of the deep learning model during heavy precipitation under typhoon and non-typhoon conditions, with different underlying physical factors controlling these mechanisms. The combination of a self-attention mechanism and interpretable deep learning provides an effective approach for refined precipitation forecasting.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108832"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding CMCC seasonal prediction system v3.5 applications to the local scale through statistical downscaling techniques","authors":"Leonardo Aragão ,&nbsp;Andrea Borrelli ,&nbsp;Silvio Gualdi","doi":"10.1016/j.atmosres.2026.108811","DOIUrl":"10.1016/j.atmosres.2026.108811","url":null,"abstract":"<div><div>The Italian Peninsula's climate is highly influenced by its complex topography and diverse regional weather systems, making high-resolution seasonal forecasting crucial for many societal sectors. Traditional seasonal prediction models, such as the CMCC SPSv3.5 (SPS), provide valuable insights but lack the spatial resolution necessary to capture local-scale climatic details. Thus, this study aims to provide a high-resolution seasonal forecast over Italy by enhancing SPS through statistical downscaling (SD) techniques tailored to the region's demand for finer-scale climate information. The SD method involves a three-step process that utilises observational datasets (ERA5 and CHIRPS) at 1/4° horizontal resolution and two machine-learning methods based on Empirical Quantile Mapping (EQM) and <em>k</em>-Nearest Neighbours (<em>k</em>NN), translating 1° SPS forecasts into high-resolution fields by matching predicted conditions to observed patterns. Both SD methods were cross-validated over the 24-year hindcast period available (1993–2016), and the results indicate significantly enhanced seasonal predictions for the Italian Peninsula, with biases about 5–6 times smaller than those of the original SPS. The main component of this improvement is spatial accuracy, which allows the identification of domain characteristics that are unnoticed in SPS. The bias evaluated by lead time, key for seasonal forecasts, showed accuracy declining from lead month 1 onward. For instance, the 2 m temperature bias increased from −0.14/−0.31/−0.85 °C in lead month 1 to −0.68/−0.71/−1.41 °C in lead month 6 (<em>k</em>NN/EQM/SPS), highlighting the challenge of maintaining predictive skill and the need for adaptive correction strategies to enhance lead-time reliability. Combining SD techniques with SPS outputs offers a solution for high-resolution seasonal predictions, supporting climate-sensitive applications by reducing forecast bias and improving spatial accuracy.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108811"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How a nocturnal cold front amplified wildfire impacts on near-surface air quality downwind of the second largest US wildfire","authors":"Sandip Pal ,&nbsp;Matthew Hamel ,&nbsp;Hassanpreet Dhaliwal ,&nbsp;Diya Das ,&nbsp;Danielle Harr ,&nbsp;Tyler Danzig ,&nbsp;Temple R. Lee ,&nbsp;Kiran Menon ,&nbsp;Nicholas E. Prince ,&nbsp;Matthew Asel ,&nbsp;Wesley Burgett","doi":"10.1016/j.atmosres.2026.108841","DOIUrl":"10.1016/j.atmosres.2026.108841","url":null,"abstract":"<div><div>Ongoing global climate change has yielded a myriad of catastrophic weather hazards, including extreme heat, drought, and severe fire weather conditions across global dryland environments. A massive wildfire ignited across the Texas Panhandle between 27 and 28 Feb 2024 (i.e., Smokehouse Creek Fire, the second largest wildfire in the US history), which consumed over 1,000,000 ha of land and resulted in an overall loss of greater than &gt;$1 billion. Understanding aerosol mixing processes and the associated kinematics near the surface and within the nocturnal boundary layer (NBL) during such wildfire events is crucial for various applications, including predicting and monitoring environmental air quality (AQ), weather forecasting and transport and dispersion modeling. This study provides, for the first time, an empirical evidence of how a nocturnal cold front amplified the wildfire impact on AQ at a site located 250 km downwind of the second largest US wildfire, yielding hazardous concentrations of fine particulate matter (PM_2.5–250 μg m⁻³). Using a combination of lidar-derived aerosol backscatter, vertical velocity and horizontal wind profiles, 10 m-tower observations of meteorological parameters, radiosonde-derived thermodynamics, and near-surface PM_2.5 measurements, our analyses revealed that narrow and intense updrafts (i.e., vertical velocity of up to 5–10 m s⁻¹) along the leading edge of a nocturnal cold front triggered the entrainment of an elevated smoke plume (∼1500-2000 m above ground) down to the surface via broader and weaker downdrafts (−0.5 to −2.0 m s⁻¹). This helped explain the transport and vertical mixing pathway of the wildfire plume near ground and aloft. Results reported enhance our understanding of NBL processes and provide critical insights for improving AQ forecasting and validating aerosol transport in dispersion models.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108841"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing the lifecycle evolution of hail-producing deep convective clouds by synergy of multi-source data","authors":"Qiang Li ,&nbsp;Hongrong Shi ,&nbsp;Yurui Xie ,&nbsp;Rubin Jiang ,&nbsp;Chaoying Wang ,&nbsp;Yupeng Teng ,&nbsp;Shuo Jia ,&nbsp;Jianwei Wen ,&nbsp;Disong Fu ,&nbsp;Jiefan Yang ,&nbsp;Xuehua Fan ,&nbsp;Jinqiang Zhang ,&nbsp;Xiaoqiong Zhen ,&nbsp;Mengqi Liu ,&nbsp;Husi Letu ,&nbsp;Hongbin Chen ,&nbsp;Xiang'ao Xia","doi":"10.1016/j.atmosres.2026.108845","DOIUrl":"10.1016/j.atmosres.2026.108845","url":null,"abstract":"<div><div>The dynamical and microphysical processes that govern the lifecycle of hail-producing deep convective clouds (DCCs) remain poorly understood, limiting severe weather prediction. Here, we dissect a severe hailstorm that occurred over Inner Mongolia using multi-source observations, including Himawari-8 satellite data, Doppler radar, and a lightning mapping network. Our analysis reveals a tightly coupled co-evolution of cloud-top microphysical properties, cloud-top kinematics, and electrical activity. A key finding is the synchronization during rapid updraft intensification of a collapsing cloud-top effective radius (from ∼40 μm to ∼20 μm) with a surge in total lightning flash rate. Rapid updrafts likely shorten particle residence time, limiting particle growth while accelerating mixed-phase collisions and non-inductive charging, thereby promoting lightning jump activity. Critically, these abrupt changes in updraft velocity and lightning activity preceded surface hailfall and peak rainfall by approximately 30–40 min and 2 h, respectively. This study provides quantitative evidence that the integration of satellite, radar, and lightning observations can elucidate the microphysical pathways leading to severe convective weather and offers valuable lead time for improved nowcasting.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108845"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shifting baselines alter trends and emergence of climate extremes across Africa","authors":"Thierry N. Taguela,&nbsp;Akintomide A. Akinsanola","doi":"10.1016/j.atmosres.2026.108839","DOIUrl":"10.1016/j.atmosres.2026.108839","url":null,"abstract":"<div><div>World Meteorological Organization baselines used to identify climate extremes are routinely updated to reflect recent climate conditions. Yet the implications of these updates for the characterization, trends, and detectability of climate extremes remain poorly understood, particularly in data-sparse and highly vulnerable regions such as Africa. Here, we quantify how updating the reference period from 1981–2010 to 1991–2020 systematically alters the characterization of temperature and precipitation extremes across the continent. Using multiple observational and reanalysis datasets (BEST, ERA5, MERRA-2, CHIRPS), we assess the sensitivity of percentile-based thresholds, long-term trends, and the Time of Emergence (ToE) to changes in the reference period. ToE is employed here as a diagnostic of detectability rather than a definitive marker of anthropogenic signal onset. Our results show that the updated baseline leads to higher temperature thresholds, resulting in a reduced frequency and slower trends for warm extremes (TX90p, TN90p), and a concurrent increase in cold extremes (TX10p, TN10p). Precipitation extremes exhibit more heterogeneous and dataset-dependent responses: trends in extreme precipitation totals (R95pTOT, R99pTOT) generally weaken, whereas intensity-based metrics (R95pINT, R99pINT) often strengthen, particularly in MERRA-2. Moreover, the choice of baseline strongly influences the estimated ToE. Warm extremes emerge 2–8 years later under the newer baseline, while cold extremes emerge earlier (by up to 15 years) due to enhanced signal-to-noise ratios. For precipitation, ToE responses vary widely across datasets and regions. In CHIRPS, the ToE of intense rainfall events is delayed, whereas in MERRA-2 it advances by over 2 decades in some regions. These results indicate that ToE estimates derived from recent decades are highly sensitive to baseline selection. By explicitly isolating the effect of baseline choice, this study provides a critical framework for interpreting extremes, reconciling dataset discrepancies, and improving the robustness of climate monitoring and risk communication across Africa.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108839"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Historical and projected variability of sea surface height and temperature: A novel downscaling approach","authors":"Mohammad Reza Nikoo ,&nbsp;Erfan Zarei ,&nbsp;Malik Al-Wardy","doi":"10.1016/j.atmosres.2026.108828","DOIUrl":"10.1016/j.atmosres.2026.108828","url":null,"abstract":"<div><div>Global climate change is intensifying sea level rise (SLR) and sea surface temperature (SST) variability, placing low-lying coastal regions at increasing risk. This study evaluates historical (1993–2023) and future (2030–2089) trends in sea surface height (SSH) and SST along Oman's coast by combining satellite data, CMIP6 GCMs, and a novel hybrid downscaling approach capable of addressing non-stationarity biases, unlike Quantile Mapping (QM) alone. Historical analysis reveals significant trends, with SSH rising to 47.86 mm/decade and SST warming by 0.48 °C/decade, driven by steric expansion, monsoon dynamics, and regional heat fluxes. Empirical Orthogonal Function (EOF) analysis identifies dominant variability modes linked to the Indian Ocean Dipole and ENSO. Among 15 CMIP6 models, HadGEM3-GC31-LL performs best in simulating SSH/SST and is selected for future projections under SSP126, SSP245, and SSP585 scenarios. For downscaling, a hybrid Quantile Mapping–Random Forest (QM + RF) model is introduced, demonstrating superior accuracy (NSE = 0.77–0.90) compared to standalone QM. Projections highlight stark spatial contrasts: under SSP126, mid-century SSH rebounds to +14.3 mm/decade after an initial decline, while SSP585 drives a relentless SST rise (0.85 °C/decade by 2089), exacerbating marine heatwaves. Coastal urban centers like Muscat and Salalah face compounded risks from SLR-induced inundation and ecosystem degradation, with northern Oman emerging as a warming hotspot. This study underscores the importance of integrating hybrid downscaling methods for regional climate adaptation and emphasizes the need for Oman to prioritize nature-based defenses and adaptive infrastructure in its National Adaptation Plan.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108828"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}