Dazhi Xi

This study employs a Hybrid Attention Transformer‐based super‐resolution model to reconstruct tropical cyclone (TC) surface wind fields. The model downscales coarse‐resolution ERA5 reanalysis data to high‐resolution HWind analyses (1998–2013), increasing the horizontal resolution by a factor of five. First, this study assesses the impact of data set partitioning strategies and different meteorological input variables on model performance. The reconstructed TC surface wind fields and the radial profiles of azimuthally averaged surface winds agree well with the HWind analysis. Key TC metrics, including the maximum sustained wind speed (MSW) and the radius of 17 m s ⁻¹ winds (R17), show high correlations with HWind (0.81 and 0.92, respectively). When independently validated against IBTrACS records over the North Atlantic (1990–2020), the model maintains robust correlations, confirming its out‐of‐sample reliability across different periods and cases. The reconstructed past TC intensity and size metrics achieved by the developed model provide new opportunities for research, demonstrating the model's future potential in TC climatology and hazard assessment studies.

A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. Unlike earlier statistical synthetic-TC models, SPIN retains intra-month variability and provides the ambient environment for each storm. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamical TC intensity model to estimate their intensity evolution based on the large-scale environment. It reproduces key features of observed TC climatology, including interannual variability, seasonal cycle, genesis and track patterns, lifetime-maximum intensity, regional landfall-intensity return periods and inter-genesis intervals. It also improves the representation of interannual TC variability in basins where ENSO-related influences are spatially mixed. Beyond individual TC events, the model demonstrates improved skill in representing multiple tropical cyclone events (MTCEs), capturing their year-to-year variability and cluster size. SPIN advances TC risk assessment by enabling compound TC hazards, such as MTCEs and beyond, to be assessed as dynamically organized events rather than statistical co-occurrences alone.

A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. A key advantage of SPIN over previous synthetic TC models is that it avoids the assumption of independence between TCs, while enabling two-way interactions between synthetic TCs and their ambient environment. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamic TC intensity model to estimate their intensity evolutions based on the large-scale environment. SPIN reproduces the observed climatology of TC activity, including interannual variability, seasonal cycle, genesis, tracks, and lifetime maximum intensity distributions. It also faithfully reproduces the observed return periods of landfall intensity across different regions, enabling its future application to TC risk assessment. Beyond individual TC events, SPIN demonstrates improved skills in representing multiple tropical cyclone events (MTCEs), including their interannual variability, peak concurrent TC count per cluster, and the spatial relationship between consecutive TCs. By circumventing the independent TC assumption and allowing for two-way TC-environment interactions, SPIN opens new potential for assessing compound hazards like MTCE and beyond.

A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. A key advantage of SPIN over previous synthetic TC models is that it avoids the assumption of independence between TCs, while enabling two-way interactions between synthetic TCs and their ambient environment. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamic TC intensity model to estimate their intensity evolutions based on the large-scale environment. SPIN reproduces the observed climatology of TC activity, including interannual variability, seasonal cycle, genesis, tracks, and lifetime maximum intensity distributions. It also faithfully reproduces the observed return periods of landfall intensity across different regions, enabling its future application to TC risk assessment. Beyond individual TC events, SPIN demonstrates improved skills in representing multiple tropical cyclone events (MTCEs), including their interannual variability, peak concurrent TC count per cluster, and the spatial relationship between consecutive TCs. By circumventing the independent TC assumption and allowing for two-way TC-environment interactions, SPIN opens new potential for assessing compound hazards like MTCE and beyond.

A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. A key advantage of SPIN over previous synthetic TC models is that it avoids the assumption of independence between TCs, while enabling two-way interactions between synthetic TCs and their ambient environment. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamic TC intensity model to estimate their intensity evolutions based on the large-scale environment. SPIN reproduces the observed climatology of TC activity, including interannual variability, seasonal cycle, genesis, tracks, and lifetime maximum intensity distributions. It also faithfully reproduces the observed return periods of landfall intensity across different regions, enabling its future application to TC risk assessment. Beyond individual TC events, SPIN demonstrates improved skills in representing multiple tropical cyclone events (MTCEs), including their interannual variability, peak concurrent TC count per cluster, and the spatial relationship between consecutive TCs. By circumventing the independent TC assumption and allowing for two-way TC-environment interactions, SPIN opens new potential for assessing compound hazards like MTCE and beyond.

A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. A key advantage of SPIN over previous synthetic TC models is that it avoids the assumption of independence between TCs, while enabling two-way interactions between synthetic TCs and their ambient environment. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamic TC intensity model to estimate their intensity evolutions based on the large-scale environment. SPIN reproduces the observed climatology of TC activity, including interannual variability, seasonal cycle, genesis, tracks, and lifetime maximum intensity distributions. It also faithfully reproduces the observed return periods of landfall intensity across different regions, enabling its future application to TC risk assessment. Beyond individual TC events, SPIN demonstrates improved skills in representing multiple tropical cyclone events (MTCEs), including their interannual variability, peak concurrent TC count per cluster, and the spatial relationship between consecutive TCs. By circumventing the independent TC assumption and allowing for two-way TC-environment interactions, SPIN opens new potential for assessing compound hazards like MTCE and beyond.

Climate change–related extreme events during the mid-late Holocene, especially around 3000 years before the present (yr B.P.), severely threatened human survival and cultural development at various locations. However, although marked social change during this period in China have also been reported to coincide with extreme disasters, the causes and impacts of these events remain unclear. Here, we aligned paleoclimate reconstructions with quantitative analyses of archeological evidence, including oracle bone scripts, together with artificial intelligence– and physics-based model simulations to uncover the causes. We found that intensified typhoon activities exerted considerable impacts on climate extremes and social change in inland China around 3000 yr B.P. These findings underscore the urgent need to improve preparedness for today’s typhoon-induced disasters in the context of accelerating climate change.

A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. A key advantage of SPIN over previous synthetic TC models is that it avoids the assumption of independence between TCs, while enabling two-way interactions between synthetic TCs and their ambient environment. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamic TC intensity model to estimate their intensity evolutions based on the large-scale environment. SPIN reproduces the observed climatology of TC activity, including interannual variability, seasonal cycle, genesis, tracks, and lifetime maximum intensity distributions. It also faithfully reproduces the observed return periods of landfall intensity across different regions, enabling its future application to TC risk assessment. Beyond individual TC events, SPIN demonstrates improved skills in representing multiple tropical cyclone events (MTCEs), including their interannual variability, peak concurrent TC count per cluster, and the spatial relationship between consecutive TCs. By circumventing the independent TC assumption and allowing for two-way TC-environment interactions, SPIN opens new potential for assessing compound hazards like MTCE and beyond.