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Abstract Understanding the responses of landfalling tropical cyclones to a changing climate has been a topic of great interest and research. Among them, the recently reported slowdown of tropical cyclone landfall decay in a warming climate engenders controversy. Here, the global climatology of landfall decay, based on the tropical cyclone best-track data available, reveals that the reported trends are uncertain and not universal, but spatial, temporal, data, and methodology dependent such that any claim of a climate trend could be misleading at present. The effective area of moisture supply from the ocean, most likely determined by the landfalling track modes, is demonstrated to be an important factor for the decay. This study provides timely essential clarifications of the current contentious understanding.

Mechanisms of tropical cyclogenesis have been studied for decades. A new one in the South China Sea, namely, PTC-STC is proposed. A subsequent tropical cyclone (STC) in the South China Sea can be induced by a pre-existing tropical cyclone (PTC) over the western North Pacific. The observations, reanalysis, and numerical sensitivity experiments suggest that the terrain of the Philippines (especially Luzon) is geographically essential to the tropical cyclogenesis and development of STC, whereas the intensity and track of PTC are conditionally decisive. If the terrain of the Philippines is replaced by sea, no STC forms. The steep mountain range of Luzon provides static blocking effect that can 1) enhance the upward motion; 2) accumulate warm moist air mass from the westerly and PTC; and 3) constrain the advection of vorticity from the PTC. Meanwhile, the northeasterly from the PTC climbs over the terrains, increases the adiabatic heating, and warms the proximity in the leeside of the mountains. These processes show that the interactions between the PTC and the terrain of the Philippines could provide favorable dynamic and thermodynamic conditions for the tropical cyclogenesis of STC in the low-to-mid troposphere of the South China Sea. Whereas, if the PTC is too strong, it could move into the South China Sea, suppressing the standalone favorable conditions for the tropical cyclogenesis of STC in the South China Sea.

The southwest vortex (SWV), a low-pressure system bringing severe rainfall in southwest China, is one of the most important synoptic systems in China. Using both the National Centers for Environmental Prediction Final (NCEP-FNL) operational global analysis dataset and the Weather Research and Forecasting (WRF) model simulation, a sophisticated SWV with dual-core structure (DCSWV) over the Sichuan Basin in 2010 was studied. The DCSWV system consisted of two cores, one near Leshan City (named “C1”) and another near Langzhong City (named “C2”). The high-resolution WRF model reproduced the life cycle of the DCSWV well. The diagnostic analysis of the vorticity budget indicated that the stretching and tilting terms played important roles in the development stage of “C1”, while the stretching and vertical advection of vorticity were the major contributors to the formation and development stage of “C2”, which implied the importance of moisture convergence and ascending motion. Sensitivity experiments showed that the DCSWV was closely associated with the release in latent heat as well as the effect of topography. The great release in latent heat provided significantly positive feedback to the DCSWV system, which was decisive to the formation and development stages of “C2”. The topography of the Tibetan Plateau and the Yun-Gui Plateau affected the location and duration of the DCSWV.

Predicting tropical cyclone (TC) activities has been a topic of great interest and research. Many existing seasonal forecasting models of TC predict the numbers of TC geneses and landfalls based on the environmental factors in the peak TC season. Here, we utilize the mainstream reanalysis datasets in 1979–2005 and propose a statistical seasonal forecasting model, namely the SYSU model, for predicting the number of TC landfalls on South China based on the preseason environmental factors. The multiple linear regression analysis shows that the April sea level pressure over the tropical central Pacific, the March-April mean sea surface temperature southwest to Australia, the March 850-hPa zonal wind east to Japan, and the April 500-hPa zonal wind over Bay of Bengal are the significant predictors. The model is validated by the leave-one-out cross validation and recent 15-year observations (2006–2020). The correlation coefficient between the modeled results and observations reaches 0.87 (p < 0.01). The SYSU model exhibits 90% hit rate (38 out of 42) in 1979–2020. The Antarctic Oscillation, and the variations of the western North Pacific subtropical high and Intertropical Convergence Zone could be the possible physical linkages or mechanisms. The model demonstrates an operational potential in the seasonal forecasting of TC landfall on South China.

Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Many studies and existing seasonal forecasting models have examined and predicted the number of TCs (including geneses and landfalls) mainly based on the environmental factors in the peak TC season. However, these predictions can be time-consuming, computationally expensive and uncertain, depending on the efficiency and predictability of the dynamical models. Therefore, here we propose an effective statistical seasonal forecasting model, namely the Sun Yat-sen University (SYSU) Model, for predicting the number of TCs (intensity at tropical storm or above) over the western North Pacific based on the environmental factors in the preseason. The nine categories comprising 103 candidate predictors in 1980–2015 (36 years) are systematically investigated. The best subset selection regression shows that the sea surface temperatures at the tropical North Atlantic and eastern North Pacific in April, the 500 hPa geopotential height difference between April and January at the open ocean southwest of Australia and the 700 hPa geopotential height at the North Pacific in April are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.89. The model is successfully validated by leave-one-out, nine-fold cross-validations, and later 5 year (2016–2020) observations. The prediction of the SYSU Model exhibits a 95% hit rate in 1980–2020 (39 out of 41), suggesting an operational potential in the seasonal forecasting of TCs over the western North Pacific.