Tropical cyclone (TC) risk has long been overlooked in Southern California due to its relatively low historical frequency. Here we couple a physics-based TC downscaling model with a probabilistic, machine learning-based landslide model to assess changes in TC rainfall and landslide risks in ten Southern California counties. Historical simulations and reanalysis data show robust agreement in downscaled TC rainfall. The return period of Hurricane-Hilary-magnitude rainfall (~100 mm) shortens by 50% from 110 years to 54 years in a future high-emission warming scenario. Eastern Pacific sea surface temperature is projected to increase by 2.7 ± 0.7 °C from 1985–2014 to 2071–2100 and, together with enhanced mid-tropopsheric moisture, contribute to increasing TC rainfall risk. All Southern California counties exhibit growth in areas exposed to landslides from 2000 to 2050. The steepest fractional increases in landslide exposure exist in low-income households with a heavy tax burden. These findings underscore a pressing need for proactive and equitable planning and mitigation strategies for TC rainfall-induced hazards.

Link to the article: https://www.nature.com/articles/s41558-026-02633-w

Photo: Yayun is presenting her paper "Atlantic Tropical Cyclone (TC) Intensity and Aerosols: A Statistical Analysis of TC environment" at the Hurricanes I: Climatology session on Friday, Mar 20, 2026.

• A machine-learning model (using XGBoost) predicts monthly 10-m wind speeds over Michigan with high accuracy (R² ≈ 0.96, RMSE ≈ 0.12 m s⁻¹) 

• The most influential predictors of wind speed in this region are local/regional features such as distance to the nearest Great Lake, surface roughness, and surface (skin) temperature—while large‐scale teleconnections (e.g., ENSO, AO, NAO) play a secondary but still measurable role

• Over the ~70-year period (1950–2020) analyzed, no statistically significant long-term trend in annual mean wind speed was found for Michigan, but the interannual variability has decreased

Globally recognized for his pioneering work related to extreme weather and climate change, Dr. Laiyin Zhu, associate professor in Western Michigan University's School of Environment, Geography and Sustainability, is being celebrated for his growing contributions to his field.

Quote from Science Advances: ONLINE COVER NASA’s SeaWiFS satellite captures a Saharan dust storm blowing 1000 miles into the Atlantic Ocean. Tropical cyclone rainfall significantly impacts coastal communities, primarily through inland flooding. Leveraging 19 years of hourly satellite observations of precipitation and other meteorological variables, Zhu et al. developed a machine-learning model to predict tropical cyclone rainfall of individual storms. They found that dust optical depth is a key predictor of rainfall and also improves model performance. The influence of dust optical depth on rainfall is complex and nonlinear and improving modeling tools will extend our understanding of this process.

• The study shows that the heavy rainfall region of tropical cyclones is expanding outward, meaning the area receiving intense rain around a cyclone is getting larger over time.
  
  

• This expansion is linked to warmer ocean and atmospheric conditions, which allow storms to hold more moisture and spread rainfall further from the storm center.
  
  

• As a result, more inland and wider surrounding regions are becoming exposed to flooding risks, even if wind intensity near the storm center does not increase.