The literature on landslide susceptibility is rich with examples that span a large number of topics. However, the component that pertains to the extension of the susceptibility framework toward space-time modeling is largely unexplored. This statement is even more valid when looking at the landslide risk context, where hardly any scientific contribution investigates risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). To perform the analyses, the data cube is organized with a mapping unit made of slope units (26,333) repeated over an annual temporal unit (for a total of 236,997). This phase already features a number of interesting modeling experiments that have hardly appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, for we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These are also combined with the projection of urban settlements and associated populations to obtain a dynamic risk model (under the assumption that vulnerability = 1). Overall, this is a unique example of such a modeling routine and a potential standard to be followed for administrations to make informed decisions on future urban development.