In general, tropical cyclone (TC) rainfall accumulation usually decreases with faster TC translation speed but increases with heavier rain rate. However, how the TC rain rate changes with translation speed is unclear. Here we show that, in all TC basins, the average TC rain rate significantly increases with translation speed. On average, the rain rate in a fast-moving TC is 24% higher than in a slow one. This difference increases with TC intensity, with category 3–5 TCs having a 42% increase while tropical depressions exhibit only a 9% increase. The increase in the average TC rain rate with translation speed is mainly caused by the TC net inflow in the lower troposphere, as well as vertical wind shear. These findings have important implications not only for a deeper understanding of rain rate changes in a translating TC but also for short-term forecasts of TC rainfall and disaster preparedness.

In this study, the extreme gradient boosting (XGBoost) algorithm is used to correct tropical cyclone (TC) intensity in ensemble forecast data from the Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) at the Shanghai Typhoon Institute (STI), China Meteorological Administration (CMA). Results show that the forecast accuracy of TC intensity may be improved substantially using the XGBoost algorithm, especially when compared with a simple ensemble average of all members in the ensemble forecast [as depicted by the ensemble average (EnsAve) algorithm in this study]. The forecast errors for maximum wind speed (MWS) and minimum sea level pressure (MSLP) have been reduced by a significant margin, ranging from 6.3% to 18.4% for MWS and from 4% to 14.9% for MSLP, respectively. The performance of the XGBoost algorithm is overall better than that of the EnsAve algorithm, although there are a few samples when it is worse. The bias analysis shows that TEDAPS underpredicts the MWS and overpredicts the MSLP, meaning that the TEDAPS underestimates TC intensity. However, the XGBoost algorithm can reduce the bias to improve the forecast accuracy of TC intensity. Specifically, it achieves a reduction of over 20% in forecast errors for both the MWS and MSLP of typhoons compared to the EnsAve algorithm, indicating the XGBoost algorithm’s particular advantage in forecasting intense TCs. These results indicate that the TC intensity forecast can be substantially improved using the XGBoost algorithm, relative to the EnsAve algorithm.

Changes in the translation speed of landfalling tropical cyclones (TCs) pose great challenges in disaster preparedness. While some recent studies have discussed the increased chance of a reduction in the annual-mean translation speed of TCs after landfall, such changes before landfall have not been systematically investigated, especially for short-term variations (that is, hour-to-day timescales). Here we show, first based on observations, that globally, a TC about to make landfall tends to accelerate towards the coast, with an average acceleration of about 0.83 m s⁻¹ per day, which means that the mean translation speed of a landfalling TC increases by ~48% during the 60-h period before landfall. Such an acceleration exists irrespective of TC intensity, seasonality and ocean basin, although its magnitude varies. Numerical simulations demonstrate that land–sea differences in surface roughness and thermal effect result in asymmetric circulation and convection in TCs, both of which are enhanced as the TC moves closer to the coast, leading to local changes in potential vorticity and thereby accelerating the storm. As this phenomenon is due to the land–sea contrast, a TC approaching the coast will probably have such an acceleration and hence it is inherent.

Most studies on tropical cyclone (TC) rain rate focus on long-term variability, yet the short-term (days or shorter) variations across the TC lifecycle, with a particular focus on the period before landfall, are most critical because they strongly influence flood risk. Using satellite data, we show that, globally, the mean rain rate of TCs increases by over 20% from 60 hours before landfall to the time of landfall. This increase occurs across hemispheres, ocean basins, intensity categories, and latitudes, although the magnitude varies. As a TC approaches the coast, land-sea thermal contrasts raise low-level humidity over land, while frictional differences enhance convergence, upward motion, and instability on the offshore side of the circulation. These conditions collectively promote increased convection and precipitation of TCs as they near landfall. Our findings critically strengthen the current understanding of TC precipitation dynamics and support more effective flood management.

The North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions.

Prediction of the rapid intensification (RI) of tropical cyclones (TCs) remains challenging. In this paper, by using information from the early period following TC formation, the rapid intensification warning index (RIWI) is developed based on the analog method. A 10‐year cross‐validation and data from Hurricane Ida (2021) are used to verify its potential application. Results show that the RIWI can efficiently discriminate between RI and non‐RI storms and has a significant positive correlation with the lifetime maximum intensity (LMI) of the TCs. By using this index, an early warning can be issued ∼30 hr before the onset of RI, which is much earlier than the predictions made using the probabilistic Statistical Hurricane Intensity Prediction System RI index. In addition, by using the RIWI as a predictor, the prediction of LMI provides an early estimate of TC severity.