Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

Pluvial floods generated by tropical cyclones (TCs) are one of the major concerns for coastal communities. Choosing Houston as an example, we demonstrate that there will be significantly elevated risk of TC rainfall and flood in the future warming world by coupling downscaled TCs from Model Intercomparison Project Phase 6 models with physical hydrological models. We find that slower TC translation speed, more frequent stalling, greater TC frequency, and increased rain rate are major contributors to increased TC rainfall risk and flood risk. The TC flood risk increases more than the rainfall. Smaller watersheds with a high degree of urbanization are particularly vulnerable to future changes in TC floods in a warming world.

The impact of tropical cyclones (TCs) has intensified with continued global warming and socio-economic development. Quantifying the TC economic exposure is a core element of economic risk assessment for TCs. The centroid of annual economic exposure to TCs in China shifted northward at a rate of 19.71 km per year from 2006 to 2020, where changes in the TC tracks contributed a northward shift of 11.22 km per year and changes in GDP distribution contributed a northward shift of 7.75 km per year. The northward shift of TC economic exposure centroid is more than twice as sensitive to the shift of GDP distribution as to that of TC tracks. The phenomenon of the northward shift in TC economic exposure is particularly evident in the subtropical zone in China. Further northward shift of TC exposure could potentially cause higher socio-economic losses in places underprepared for TC hazards. Our result provides references for TC disaster mitigation and preparedness in China.

Extreme precipitation from tropical cyclones can generate large-scale inland flooding and cause substantial damage. Here, we quantify spatiotemporal changes in population risk and exposure to tropical cyclone precipitation in the continental eastern United States over the period 1948–2019 using high-resolution in-situ precipitation observations. We find significant increases in the magnitude and likelihood of these extreme events due to increased rainfall rates and reduced translation speeds of tropical cyclones over land. We then develop a social exposure index to quantify exposure and risk of tropical cyclone precipitation as a function of both physical risk and socio-economic activities. Increased social exposure is primarily due to the increased risk of tropical cyclone precipitation, but there are regional differences. We identify exposure hotspots in the south-eastern United States, where rapid population growth and economic development amplify societal exposure to tropical cyclone hazards. Our multi-scale evaluation framework can help identify locations that should be targeted for mitigation and adaptation activities to increase their climate resilience.

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms.

We examined a 60-year record of daily precipitation and river discharge related to tropical cyclones (TCs) in four watersheds undergoing land use and land cover change near Houston, Texas. Results show that TCs are responsible for ∼20% of the annual maximum discharge events in the four selected watersheds. Although there are no trends in TC precipitation, increasing trends were observed in daily extreme discharge and TC-related discharge. The more developed watersheds (Whiteoak Bayou and Brays Bayou), tend to have higher extreme discharge and steeper trends in extreme discharge than the less developed watersheds (Cypress Creek). Increases in TC-related extreme discharges correspond with increases in developed land and decreases in vegetated land between 1980 and 2006. Therefore, changes in land cover/use in watersheds near Houston are a major cause of the increased flooding risk in recent years