Fall armyworm is recognized as one of most highly destructive global agricultural pests. In January 2020, it had first invaded Australia, posing a significant risk to its biosecurity, food security, and agricultural productivity. In this study, the migration paths and wind systems for the case of fall armyworm invading Australia were analyzed using a three-dimensional trajectory simulation approach, combined with its flight behavior and NCEP meteorological reanalysis data. The analysis showed that fall armyworm in Torres Strait most likely came from surrounding islands of central Indonesia on two occasions via wind migration. Specifically, fall armyworm moths detected on Saibai and Erub Islands might have arrived from southern Sulawesi Island, Indonesia, between January 15 and 16. The fall armyworm in Bamaga most likely arrived from the islands around Arafura Sea and Sulawesi Island of Indonesia, between January 26 and 27. The high risk period for the invasion of fall armyworm is only likely to have occurred in January–February due to monsoon winds, which were conducive to flight across the Timor Sea towards Australia. This case study is the first to confirm the immigration paths and timing of fall armyworm from Indonesia to Australia via its surrounding islands.

Dengue fever, influenced by climate dynamics and human mobility in nonendemic regions, remains poorly understood. We assessed the effects of large-scale climate features on domestic dengue outbreaks using data from China (2013–2021) and projections for 2023–2028, incorporating climate, human mobility, and environmental factors. A significant positive correlation (r = 0.89, P < 0.01) was found between domestic dengue incidence and overseas imported cases. Significant associations were also noted with domestic (F2.96, 25.78 = 518.03, P < 0.01) and international human mobility (F2.77, 25.78 = 66.84, P < 0.01), the Indian Ocean Dipole (IOD) index (F2.98, 25.78 = 522.84), and vectorial capacity (F2.92, 25.78 = 338.74, P < 0.01). IOD is the most influential climate feature, with a trend of intensification over time. The Extreme Gradient Boosting (XGBoost) algorithm incorporating IOD (r2 = 0.56) predicted outbreaks in 2025, 2026, and 2028. Our findings reveal that large-scale climate phenomenon, notably the IOD, and human mobility significantly influence dengue domestic outbreaks in China through a “dislocated impact.”