Fall Armyworm (FAW) (Spodoptera frugiperda), an endemic pest species in the tropical and subtropical Americas, is expanding its geographical range, with recent invasions reported in Sub-Saharan Africa and Asia, and their interconnection with Europe. With a host range of over 80 plants, the pest inflicts most damage to staple cereals, non-cereal crops, and pasture grasses impacting food security and livestock production. Despite of its disruptive potential, little attention has been given to understand potential impacts of the projected climate change at local and global scales, hence the aim of this study. Based on current incidence records in the Americas and Africa and the most biologically relevant climatic data, we calibrated and used seven (7) correlative model algorithms: profile (BIOCLIM), regression (General Linear Models) and machine learning (Maxent, Classification and Regression Trees, RandomForest, Boosted Regression Trees and Support Vector Machines) to model the niche and distribution of S. frugiperda under current and future climate change scenario. FAW showed widespread spatial distributional patterns, covering all the six continents. Climate change is likely to increase FAW outbreaks and damage in Central and Western Europe, and NW Pacific and East Asia, while Central Africa, Caribbean, Southern Africa, South America, Mesoamerica, Western Indian Ocean, Eastern Africa and Canada will likely experience reduced FAW damage. FAW integrated and sustainable management efforts need to be instituted, as a large proportion of the world was projected to be favorable for FAW currently, and in the foreseeable future.

Keywords: Africa, Future Climate, Modeling, Precipitation, Species distribution models, and Temperature.