Kilometer-scale dynamical climate downscaling is increasingly important for climate-risk information to rapidly urbanizing regions such as Southeast Asia (SEA), where megacities are vulnerable to extreme rainfall, heat stress, and urban climate hazards. However, the extent to which explicit urban representation improves convection-permitting regional climate simulations remains insufficiently quantified. Here, we developed and evaluated a 4-km convection-permitting Weather Research and Forecasting (WRF) modeling framework over SEA to investigate the sensitivity of precipitation, near-surface temperature, and surface wind to urban land-use representation and urban canopy parameterization. Sensitivity experiments were conducted using MODIS urban land use and high-resolution Local Climate Zone (LCZ) datasets with Bulk, SLUCM, BEP, and BEPBEM schemes. Urban drag parameterization and rural surface wind diagnosis were modified to address persistent surface wind-speed biases.Simulations were compared to ERA5 and evaluated against station observations in five metropolitan regions: the Pearl River Delta (PRD), Bangkok, Manila, Kuala Lumpur, and Jakarta. Results show urban representation influences variables despite convection-permitting resolution. LCZ better represents urban extent and morphology than MODIS by improving thermal patterns. Incorporating multilayer urban canopy physics, particularly BEPBEM, improves heat island intensity and temperature, increases heavy-precipitation frequency and better captures precipitation extremes. Modified urban drag and rural wind diagnosis reduce wind overestimation. Although the short evaluation period limits climatological generalization, the results provide physically consistent evidence that urban morphology and canopy processes remain important for kilometer-scale modeling of SEA megacities. The combination of LCZ, BEPBEM, and modified drag treatment provides the most balanced performance and supports long-term convection-permitting downscaling over SEA.

Hong Kong's extensive coastline and dense economic development render it highly vulnerable to tropical cyclone-induced storm surges, a risk exacerbated by projected global warming and rising sea levels over the 21st century. This study pioneers the use of a very high-resolution (100-m) air-wave-ocean coupled model, integrating convection-resolving atmosphere and three-dimensional ocean components, to project worst-scenario storm surges in Hong Kong by the 2040s, within the period in the next twenty years that decision-makers and stakeholders are most concerned with. Three experiments simulate Typhoon Mangkhut (2018) under distinct conditions: (1) Future_Warming, assessing upper-bound atmospheric and oceanic warming; (2) Strait_Traveler, modeling a trajectory bypassing Luzon Island; and (3) Combo, combining both factors. Results project storm surges exceeding astronomical tides by approximately 4.0 m in Victoria Harbour, 5.2 m in Deep Bay, and 6.2 m in Tolo Harbour, with Tolo Harbour potentially experiencing multiple flooding peaks within 12 h due to the seiche effect, which has not been reported in the past. These findings, among the first to leverage such high-resolution and fully coupled configurations, highlight the critical need for enhanced monitoring, infrastructure resilience, and robust disaster preparedness to mitigate the escalating socioeconomic impacts of future storm surges in Hong Kong.

The variation between Earth's perihelion and aphelion causes seasonal fluctuations in the total solar radiation (SR) received by the planet. Our analysis of observational data and Community Earth System Model (CESM) simulations indicates that the temporal phase of atmospheric total energy in each hemisphere tends to follow the annual cycle of SR in each hemisphere with a delay of less than a month. However, the amplitude of the annual cycle of atmospheric energy in the Southern Hemisphere (SH) is weaker than that in the Northern Hemisphere (NH), despite the annual cycle of SR in the SH being 7% stronger than that in the NH. The stronger annual cycle response of atmospheric energy in the NH, despite its weaker annual cycle of SR, results in a six-month delay in the annual cycle of the global mean total atmospheric energy relative to the annual cycle of total SR received by Earth, as shown in both observations and CESM simulations.An aqua planet climate simulation reveals that the global and hemispheric mean atmospheric total energy in each hemisphere follows the SR annual cycles in both timing and amplitude. The contrast between the CESM and aqua planet simulations indicates that the difference in land-ocean distribution between the two hemispheres causes the asynchronous global atmospheric energy response to SR forcing. Using a dry radiative-convective coupled atmosphere-surface climate model, we confirm that this asynchronous global atmospheric energy response to SR forcing is driven by the hemispheric asymmetry in land-ocean distribution.

The Pearl River Delta (PRD) in southern China is a densely populated subtropical region with hot and humid summers. Conventional heatwave definitions based solely on dry-bulb temperature often underestimate heat risk in humid climates, yet few studies apply humidity-sensitive and temperature-based indices within a unified event-based framework. Here, we apply the Excess Heat Factor based on the Heat Index (HI-EHF) and 2 m air temperature (T2-EHF) in parallel to distinguish three exclusive heatwave event types (HI-only, T2-only, and Hybrid) Using bias-corrected CMIP6 ensembles dynamically downscaled with WRF, we analyze heatwaves in the 2010s, 2040s, and 2090s. Heatwaves are defined as periods of at least three consecutive days when either HI-EHF or T2-EHF is positive. Results show event composition shifts toward the Hybrid type. Under fixed 2010s reference thresholds, Hybrid accounts for 98.6% of PRD heatwave land grid days in the 2090s, while HI-only and T2-only account for 0.8% and 0.6%, consistent with mean-state migration and HI nonlinearity. HI-only events are short, thermally moderate yet humid episodes, rooted in pre-moistened land conditions and sustained by marine inflow. T2-only events remain radiation-dominated, though their dryness weakens as background humidity increases toward the late century. Hybrid events become more frequent, longer, and more intense, clustering in increasingly hot and humid regimes characterized by elevated low-level moisture and strong insolation. These findings highlight the importance of incorporating humidity-sensitive metrics in humid subtropical regions, where a multidimensional perspective is critical for accurate risk assessment and adaptation strategies.

Topography plays an important role in shaping Earth’s climate system; however, the relative contributions of bathymetry, land–sea distribution, and continental topography remain uncertain. To isolate the impact of ocean topography on the present oceanic and atmospheric system, we conducted an aquaplanet simulation with realistic bathymetry using the fully coupled Community Earth System Model v1.2.2, in which land was replaced by a 10-m-deep ocean, while realistic seafloor bathymetry was preserved (hereafter BATHY). The model results show that, compared with the real Earth, BATHY retains a similar large-scale ocean circulation but exhibits an overall strengthening. The strengthened subtropical cell, weakened upper circulation in the Southern Ocean, and deepened Atlantic meridional overturning circulation lead to a warmer Antarctic and a colder Arctic, resulting in larger temperature gradients and stronger westerlies in the Northern Hemisphere but smaller gradients and weaker westerlies in the Southern Hemisphere. Consequently, a more hemispherically symmetric climate relative to the real Earth is established. However, hemispheric asymmetry persists in the BATHY simulation, with a surface pattern similar to that of the real Earth. The BATHY experiment reveals the role of ocean topography in shaping climate and modulating hemispheric asymmetries in a land-free coupled system. Ocean topography not only regulates the oceanic and atmospheric systems through ocean circulation but also contributes to hemispheric asymmetry in the current climate system. These findings also have implications for interactions among ocean circulations and for understanding climate variability.

To understand the role of the Antarctic Circumpolar Current (ACC) in the polar seasonality and its remote effect on the Arctic climate, we use the Community Earth System Model to perform Drake Passage (DP) open and closed experiments. Model results illustrate that in the opened DP, the ACC and Atlantic Meridional Overturning Circulation (AMOC) strengthen, leading to a colder Antarctic and a warmer Arctic. Notably, the temperature changes in both the Antarctic and the Arctic show significant seasonal differences, with the largest polar response during the cold seasons. Around the Antarctic, both the ACC and overturning circulation exhibit stronger acceleration in winter than in summer, causing more pronounced cooling in winter. Furthermore, negative seasonal energy transfer mechanism amplifies this cooling. In contrast, around the Arctic, the AMOC and ocean heat transport show relatively insignificant seasonal variation. Instead, it is the downward latent and sensible fluxes that induce amplified winter warming.

Abstract The Australian summer monsoon (AUSM) is the strongest monsoon in the Southern Hemisphere and it is greatly influenced by the climate conditions in the Indo‐Pacific and adjacent regions. Inspite of the substantial studies of the monsoon, the linkage between the AUSM and the high‐latitude Southern Ocean climate has not been fully understood. This study investigates how AUSM rainfall is affected by the Antarctic Circumpolar Current (ACC) by simulating scenarios with a closed and an opened Drake Passage with the Community Earth System Model. It is found that AUSM precipitation decreases as a result of reduced local humidity caused by a strengthening ACC. An opened Drake Passage leads to strengthening ACC and Atlantic Meridional Overturning Circulation, which in turn creates an El Niño‐like state in the Pacific. This weakens the Walker Circulation, resulting in reduced local moisture, anomalous subsidence over the AUSM region, and a subsequent decrease in monsoon precipitation.

Recent findings show a remarkable linkage between the Northern Hemisphere and Southern Hemisphere climates. Previous studies have focused on the impact of the climate change in the northern high-latitudes on that in the Southern Hemisphere, but few studies concerned the impact of Southern Ocean circulation on the Northern Hemisphere, especially the Arctic climate. In this study, we close the Drake Passage (DP) to slow down the Antarctic Circumpolar Circulation (ACC) in the fully coupled Community Earth System Model, to investigate the impact of weakened ACC on the Northern Hemisphere.Two model experiments, DP opened and DP closed experiments, are performed. Relative to the DP opened case, a warmer Antarctic with less sea ice cover but a colder Arctic with more sea ice cover appear in the DP closed case resulting from weaker ACC and Atlantic Meridional Overturning Circulation (AMOC). Especially, the changes in surface air temperature in the two poles are largest in winter.Compared to the DP opened case, the anomalous southward heat transport by weakened ACC is largest in winter, contributing to the winter amplification in the Antarctic. However, the seasonal difference in AMOC change is insignificant. To understand the winter amplification in the Arctic, we further analyze local surface heat flux changes in the Arctic. The anomalous downward longwave radiation and sensible and latent heat fluxes are stored in the ocean in summer and released to the atmosphere in the following winter. Although the ocean heat content warms the surface, the upward sensible and latent heat fluxes cool the surface more significantly in winter. This local atmosphere-ocean-ice interaction contributes to the winter amplification in the Arctic. When DP is closed, the westerlies become stronger and move poleward in the Northern Hemisphere because of the increased meridional temperature gradients, especially in winter. The change in surface temperature also contribute to the weakening of Aleutian Low in winter. The warming in the Antarctic and the cooling in the Arctic leads to the notable weakening of Hadley circulation in the Southern Hemisphere. Additionally, compared to the DP opened case, the Intertropical Convergence Zone shifts southward and the Walker circulation and trade winds over the Pacific strengthen. These results shed light on understanding the interhemispheric interaction and the pole-to-pole connection.

Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms, which map sensory inputs directly to driving actions, thereby enhancing the robustness and adaptability of autonomous vehicles. However, these models often sacrifice interpretability, posing significant challenges to trust, safety, and regulatory compliance. To address these issues, we introduce DRIVE -- Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving, a comprehensive framework designed to improve the dependability and stability of explanations in end-to-end unsupervised autonomous driving models. Our work specifically targets the inherent instability problems observed in the Driving through the Concept Gridlock (DCG) model, which undermine the trustworthiness of its explanations and decision-making processes. We define four key attributes of DRIVE: consistent interpretability, stable interpretability, consistent output, and stable output. These attributes collectively ensure that explanations remain reliable and robust across different scenarios and perturbations. Through extensive empirical evaluations, we demonstrate the effectiveness of our framework in enhancing the stability and dependability of explanations, thereby addressing the limitations of current models. Our contributions include an in-depth analysis of the dependability issues within the DCG model, a rigorous definition of DRIVE with its fundamental properties, a framework to implement DRIVE, and novel metrics for evaluating the dependability of concept-based explainable autonomous driving models. These advancements lay the groundwork for the development of more reliable and trusted autonomous driving systems, paving the way for their broader acceptance and deployment in real-world applications.

Abstract Recent advancements in state‐of‐the‐art generative deep‐learning models, particularly diffusion models, have significantly enhanced the capability to produce realistic and diverse synthetic images and videos. These advancements have had a profound impact on fields such as computer vision and natural language processing. In this study, we leverage this cutting‐edge generative model to refine Numerical Weather Prediction (NWP) precipitation outputs. By conditioning the generative model with fundamental meteorological variables simulated by the Weather Research and Forecasting model, we aim to reproduce the high‐resolution satellite precipitation product, specifically CMORPH. Benefiting from the superior ability of generative diffusion models to learn the distribution of target data, these models excel in providing detailed and accurate precipitation estimations over the raw NWP outputs and traditional predictive models. With this presented pipeline, we provide valuable insights and practical tools for refining precipitation forecasting while preserving its extremities and variability thus better guiding decision making regarding weather dependent activities.

Accurate air quality forecasting is crucial in providing reliable early warning information to the public. However, predictions generated by three-dimensional chemical transport models, such as the widely used Community Multiscale Air Quality (CMAQ) model, often exhibit considerable biases compared to observations. Post- processing techniques can substantially enhance the forecasting skill of air quality models. In this paper, a hybrid deep learning model, namely AirQFormer, is proposed as an end-to-end bias correction method to improve the accuracy and reliability of regional CMAQ forecasts over 72 h. The performance of AirQFormer was evaluated based on ozone observations from the Greater Bay Area in Southern China for the year 2023. AirQFormer demonstrated superior accuracy at the temporal scale compared to the CMAQ model and the long shortterm memory (LSTM) model over the 72-hour forecasting period. It achieved an average reduction of 35 % (5.2 ppbv) in mean absolute error (MAE) and 33 % (6.5 ppbv) in root mean square error (RMSE) compared to the CMAQ model. Additionally, it showed a 12 % reduction (1.1 ppbv) in MAE and an 11 % reduction (1.4 ppbv) in RMSE compared to the LSTM model. At the spatial scale, AirQFormer outperformed both the CMAQ model and traditional spatial bias correction methods, with MAE values being 31 % (4.5 ppbv) and 5 % (0.5 ppbv) lower than those of the CMAQ model and traditional methods, respectively. Regarding peak value forecasting, AirQFormer exhibited notable improvements compared to the CMAQ model. The false alarm rate of AirQFormer is 12 % lower than that of the CMAQ model, indicating a more accurate identification of episode events. These results demonstrate the effectiveness of our proposed model in improving air quality predictions.

Accurate accident anticipation remains challenging when driver cognition and dynamic road conditions are underrepresented in predictive models. In this paper, we propose CAMERA (Context-Aware Multi-modal Enhanced Risk Anticipation), a multi-modal framework integrating dashcam video, textual annotations, and driver attention maps for robust accident anticipation. Unlike existing methods that rely on static or environment-centric thresholds, CAMERA employs an adaptive mechanism guided by scene complexity and gaze entropy, reducing false alarms while maintaining high recall in dynamic, multi-agent traffic scenarios. A hierarchical fusion pipeline with Bi-GRU (Bidirectional GRU) captures spatio-temporal dependencies, while a Geo-Context Vision-Language module translates 3D spatial relationships into interpretable, human-centric alerts. Evaluations on the DADA-2000 and benchmarks show that CAMERA achieves state-of-the-art performance, improving accuracy and lead time. These results demonstrate the effectiveness of modeling driver attention, contextual description, and adaptive risk thresholds to enable more reliable accident anticipation.

Black carbon (BC) and brown carbon (BrC) have been considered light-absorbing components of particulate matter and affect weather and climate. Biomass burning (BB) emission from Southeast Asia (SEA) is a key source of BC and BrC on the planet. In this study, the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) two-way coupled model was used with the Global Fire Emissions Database Version 4, to investigate the direct radiative effect (DRE) of BC and BrC in March 2015 over SEA. The Rapid Radiative Transfer Model for the Global Circulation Model was employed in the WRF-CAMQ to calculate the aerosol optical properties in 14 shortwave spectral bands. Parameterization of the light absorption property of BrC described by Saleh et al. (2014) is coded and embedded into the WRF-CMAQ. The light absorption property of BrC is determined by the BB BC to organic carbon ratio in each grid and each time step, which is more in line with the smog chamber experiments compared to the originally fixed coefficient in the model. Experiments with and without BC/BrC DRE were conducted. Preliminary results show that the monthly mean DRE from BB BC can reach 18.3 W/m2 in the Indochina region and 3.0 W/m2 in southern China, decreasing the surface temperature by up to 0.2 and 0.1 °C, respectively. The monthly DRE from BB BrC can reach 1.3 W/m2 in the Indochina region but only around 0.1 W/m2 in southern China. Meanwhile, the maximum instant DRE of BrC can reach 10.0 W/m2, which is expected to exert a local synoptic scale influence.

Cities face twin challenges of changing climate and urban heat island (UHI) effect, with the environmental implications of urbanization still unclear. This study employs the Weather Research and Forecasting (WRF) model to examine the impacts of urbanization in the Greater Bay Area (GBA) during warm and cold seasons in past (2000) and near-future (2030) scenarios [i.e., under shared socioeconomic pathways (SSP): SSP1-26, SSP2-45, and SSP5-85]. Datasets from the World Urban Database and Access Portal Tools (WUDAPT) were incorporated into the WRF model to improve the representation of urbanization effects on weather patterns. Our findings indicate that urban expansion significantly increases urban temperatures and decreases wind speed across selected climate change scenarios. The spatially averaged urban temperatures in the fine resolution domain for 2030 could rise by 0.9 degrees C (warm season) and 2.4 degrees C (cold season), respectively, and urban wind speed decreases by similar to 3 m/s under the SSP5-85 scenario. In cold season, the UHI effect could last over 20 h, while urban area may experience cooler nighttime temperatures than rural areas in warm season. Moreover, the future scenario predicts higher daytime O-3 levels due to the warming effects of climate change and urbanization, but lower nighttime levels in warm season, attributed to intensified south-easterly sea breeze and background winds, when compared to the past scenario. This study highlights the importance of incorporating urbanization and climate change in future urban atmospheric environment studies, and underscores that urban climate change adaptation and mitigation should consider extra impacts on built-environment caused by urbanization.

This study investigates the impacts of urban-induced anthropogenic heat (AH) and surface roughness on Tropical Cyclone (TC) Victor (1997) using the Weather Research and Forecasting Model. TC Victor originated in the South China Sea and made landfall over the Greater Bay Area (GBA) megacity. The storm was characterized by slow movement, a weakly organized structure, and a small size. Three parallel experiments were conducted in the following setups: "Nourban," where urban areas in the GBA were replaced by croplands, "AH0" ("AH300"), in which the diurnal maximum AH was set to 0 (300 W/m2) in urban locations. The urbanization effect during the landfall period results in a notable reduction in both the Power Dissipation Index and Integrated Kinetic Energy of the storm. A Lagrangian particle dispersion model further demonstrates that from 33 to 17 hr prelandfall, the entrainment of AH-affected warm airflow into the TC's circulation leads to a decrease in both the convective available potential energy and surface latent heat flux in the western quadrant of the TC, thus weakening the cyclone's intensity. Furthermore, from 12 hr prelandfall to 8 hr postlandfall, there is an intense influx of AH-affected airflow into the TC's primary circulation, resulting in a decrease in relative humidity and a stronger asymmetrical structure, declining the TC intensity. Concurrently, urban surface roughness contributes to a decrease in postlandfall storm intensity through frictional energy dissipation. This study underscores the critical role of AH and urban roughness in modulating TC intensity for storms with specific characteristics, emphasizing the need for deeper insights into urban-TC dynamics.

Abstract Both historical observations and recent modeling studies reveal a faster warming in the Arctic compared to the Antarctic. To understand the role of the Antarctic Circumpolar Circulation (ACC) in this warming asymmetry, we simulate the climate mean state and climate response to doubled CO2 under different climate mean ACC states by closing or opening the Drake Passage (DP) with the Community Earth System Model. From closed to open DP, a stronger climate mean ACC leads to a stronger climate mean Atlantic Meridional Overturning Circulation (AMOC), as well as a colder Antarctic but a warmer Arctic in the climate mean state. The less climate mean sea ice coverage in a warmer Arctic implies less extensive sea ice melting under global warming. This causes a reduced asymmetry in warming between the two poles in response to the doubled CO2.

The Pearl River Delta (PRD) region is highly vulnerable to tropical cyclone (TC)-caused coastal hazards due to its long and meandering shoreline and well-developed economy. With global warming expected to continue or worsen in the rest of the twenty-first century, this study examines the TC impact on the PRD coastal regions by reproducing three intense landfalling TCs, namely Vicente (2012), Hato (2017), Mangkhut (2018), using a sophisticated air-wave-ocean coupled model of high spatial resolution (1-km atmosphere and 500-m wave and ocean). The simulations are conducted using present-day reanalysis data and the same TCs occurring in a pseudo-global warming scenario projected for the 2090s. Results indicate that the coupled model accurately reproduces the air-wave-ocean status during the TC episodes. The 2090s thermodynamic status effectively increases the intensity of intense TCs, leading to more severe coastal hazards including gale, rainstorm, and storm surges and waves. On average, the maximum surface wind speed within 50–200 km to the right of the TC center can increase by 4.3 m/s (+22%). The 99th and the 99.9th percentile of accumulated rainfall will increase from 405 to 475 mm (+17.3%), and from 619 to 735 mm (+18.6%), respectively. The maximum significant wave height at the ocean is lifted by an average of 57 cm (+13.8%), and the coastline typically faces a 40–80 cm increase. The maximum storm surges are lifted by 30–80 cm over the open sea but aggravate much higher along the coastline, especially for narrowing estuaries. For Typhoon Vicente (2012), there is more than a 200 cm wave height increase observed both at open sea and along the coastline. In the 2090s context, a combination of mean sea level rise, storm surge, and wave height can reach more than 300 cm increase in total water level at certain hot-spot coastlines, without considering the superposition of spring tides.

Abstract Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.