In recent years, through the availability of remotely sensed datasets, it has become possible to conduct global-scale landslide risk assessment. This talk evaluates the potential of NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) products to advance our understanding and predicting ability of rainfall-triggered landslides. Early results show that the landslide occurrence is closely associated with the spatial patterns and temporal distribution of TRMM rainfall characteristics. Particularly, the number of landslides triggered by rainfall is related to rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms. For the purpose of prediction, an empirical TMPA-based rainfall intensity-duration threshold is developed. TMPA precipitation data, available both at real-time and from a long-term archive (8+ years), are used to evaluate the accuracy of location and timing of potential landslides by checking with the intensity-duration threshold and antecedent rainfall conditions. These experimental findings, in combination with landslide susceptibility information based on satellite-based land surface information, form a starting point towards an operational landslide monitoring/warning system around the globe.