Xuezhi Tan

Accurate spatiotemporal wind speed prediction is essential for improving wind energy utilization and ensuring power grid security. However, existing methods often rely on incorporating multiple meteorological variables, which increases model complexity and obscures the role of large‐scale circulation in guiding local wind evolution. This study proposes a physics‐aware gated spatiotemporal fusion network (PAG‐STFN) for 24‐hr wind speed prediction over southern China and its coastal regions. By introducing mean sea level pressure to represent the large‐scale circulation background, the model adaptively modulates historical wind field features. Results show that PAG‐STFN yields smaller mean errors than the baseline models, with particularly clear advantages at lead times of 6 hr and beyond. The model also preserves the seasonal wind field structures over the study region and demonstrates good predictive performance at offshore locations near Jinwan and Changle. Analysis of a cold wave event further illustrates that PAG‐STFN can adaptively regulate the intensity and spatial location of the injected background information, thereby contributing to improved prediction accuracy. Meanwhile, PAG‐STFN introduces only a marginal increase in model parameters while maintaining fast inference, highlighting its practical potential in wind power prediction and operational dispatch.

Coastal near‐surface wind speed (NSWS) largely relates to the hub‐height wind speed for offshore wind production. To investigate the coherence between regional NSWS and large‐scale climate anomalies such as El Niño‐Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Pacific‐Darwin Oscillation Index (NOI), North Atlantic Oscillation (NAO), and Western Hemisphere Warm Pool (WHWP), we comprehensively conducted various wavelet analyses and composite analyses to coastal NSWS and six climate indices for the period 1960–2019. The results show that, although a reversal in wind speed occurred in 2002 for most regions of China, NSWS in the coastal areas of China has declined at the rate of −0.0189 m s ⁻¹ yr. ⁻¹ during 1960–2019. Significant interannual and interdecadal oscillations are observed in both coastal NSWS and climate indices. For NSWS PC1 (the first principal component) which accounts for 44.48% of the total variance of NSWS, significant interannual oscillations with the period of approximately 2–4 years were observed during the 1970s and 2010s, and oscillations with periods 8–16 years occurred during 2000–2019. ENSO, NOI, and WHWP indices all exhibit strong periodic signals of approximately 2–6 years in coherence with NSWS PC1 from 1960 to 2019, while NAO demonstrates a synchronous variation with NSWS PC1 at an 8–16‐year timescale, particularly after 1990. Partial wavelet coherence analyses show that ENSO is strongly linked to NOI and WHWP which show strong coherence to NSWS PC1, while a weak connection exists between NAO and other climate indices at interdecadal time scales. The leading influences of ENSO‐related thermal anomalies, combined with the lagging behaviour of NOI, suggest that oceanic changes can drive atmospheric circulation patterns, while pressure system variations tend to precede changes in coastal NSWS. This aligns with the rapid atmospheric response to oceanic changes and reflects the physical process of pressure gradient driving wind fields. Meanwhile, the strong associations between low‐frequency climate signals and NSWS PC1, especially over the 8–20 years scale, suggest that these indices hold potential as reliable predictors for long‐term NSWS prediction.

As anthropogenic forcing intensifies, soil moisture droughts have become prevalent across many regions. Soil moisture anomalies can influence near-surface air temperature by modulating both energy and carbon fluxes. However, the annual temperature response to soil moisture anomalies in the year following a soil moisture drought remains unclear. Here, we show that annual soil water loss in the year after soil moisture droughts induces a larger annual warming effect than the annual cooling from an equivalent water gain. This asymmetric response arises only when the effects of anthropogenic greenhouse gas forcing are included, where vegetation is more strongly suppressed after soil moisture drought than enhanced after soil moisture gaining, contributing to reduced transpiration and carbon uptake that collectively amplify warming. More than 60% of global land areas exhibit the asymmetry. Our findings suggest that past climate extremes may accelerate future warming by impairing vegetation’s capacity to regulate biophysical and biogeochemical processes.

Knowing the status of emergency capacity for disaster risk reduction helps the government and stakeholders to minimize vulnerabilities and disaster risk. However, there is no widely applied methodology for emergency capacity assessment. This study develops a multidimensional framework integrating vulnerability, susceptibility, and adaptability assessments to evaluate regional flood emergency capacity. The information quantity method, the Maxent model, and the entropy‐weighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) are used to assess the regional vulnerability, susceptibility, and adaptability of disaster emergency capacity, respectively. These capacity features are then integrated into the overall disaster capacity using the analytic hierarchy process (AHP)‐entropy method. The exemplary application in Zengcheng District (ZCD), China shows the effectiveness of the proposed method. The identified vulnerability hotspots of flood over ZCD are located in Xintang and southern Yongning, driven by their high road density, GDP per capita, residential disposable income, and high population density. Susceptibility modeling revealed land use, Vegetation Index, and elevation as the dominant drivers, with Xintang exhibiting the highest risk. Adaptability assessments highlighted superior resilience in Paitan, Zhongxin, and Shitan, driven by enhanced disaster‐preparedness investments. Integrated analysis prioritized emergency resource storage capacity, drainage capacity, and emergency rescue capacity as critical determinants of flood response capacity. Lower emergency capacity is concentrated in Zhongxin, Xiaolou, Zhenguo, and Zhucun. This paper constructs a multidimensional assessment framework covering the entire chain of pre‐disaster prevention, in‐disaster response, and post‐disaster recovery, to systematically evaluate a region's comprehensive emergency capacity, providing potentially feasible insights for urban areas vulnerable to flood disasters.

Offshore wind energy exhibits strong spatiotemporal variability fundamentally driven by large‐scale meteorological patterns (LSMPs), but the mechanisms linking LSMPs to both wind energy variability and extreme wind events remain insufficiently understood. Focusing on the offshore region of China, this study uses the self‐organising map algorithm to identify the dominant seasonal LSMP transition pathways affecting wind energy and to establish explicit links between specific LSMPs and the spatial distribution of extreme wind events. The results show that wintertime transitions involving Siberian High‐enhanced pathways contribute to persistently strong winds and stable power output. In other seasons, dominant LSMP evolution pathways lead to pronounced spatial redistribution of wind energy, causing the optimal regions for offshore wind power generation to alternate between the northern and southern seas. LSMPs also regulate the occurrence of extreme wind speed events, as regions with positive capacity factor anomalies typically experience lower frequencies of low wind speed extremes. A further decomposition of the interannual variability in seasonal capacity factor and the frequency of extreme wind speed events shows that changes in wind energy characteristics under a given LSMP (within‐pattern component) are the primary drivers of their interannual variability. These findings suggest that future research should pay greater attention to how evolving thermal gradients and boundary‐layer conditions under global warming may modulate offshore wind energy within individual circulation patterns.

The evaluation of offshore wind energy resources is important to the construction of offshore wind power facilities. In this paper, using four models from CMIP6 and the ERA5 reanalysis dataset, a deep learning model termed SwinWind was developed and proposed to evaluate future offshore wind energy resources in Southeastern China for the periods 2020–2050 and 2070–2100. The feature extraction capability of the Swin Transformer was utilized to construct a bias correction and downscaling framework. This approach achieves performance comparable to existing high-cost models while significantly reducing computational costs and complexity. The SwinWind model corrected most of the biases and effectively learned spatial relationships, successfully performing the downscaling task. Based on future wind speed projections derived from the SwinWind model, this study presents a comprehensive evaluation of offshore wind resources, examining five critical dimensions: resource abundance, efficiency, stability, the impact of extreme winds, and economic feasibility. It is projected that offshore wind resources around Shanghai, Jiangsu and Zhejiang will experience a decline in the 21st century, while offshore wind resources around the Guangdong, Fujian and the Beibu Gulf show an increasing trend. The evaluation index shows that the coastal areas of Guangdong and the southern coastline of Taiwan are the most suitable locations for wind power exploitation. The Taiwan Strait, which has the highest wind energy density, is not the best spot due to its extreme wind speed and unstable wind resources. This study provides an important reference for the location of wind farms with practical application value.