Landslide susceptibility models are fundamental components of landslide risk management strategies. These models typically assume that landslide occurrence is time‐independent, even though processes including earthquake preconditioning and landslide path dependency transiently impact landslide occurrence. Understanding the temporal characteristics of landslide occurrence remains limited by a lack of systematic investigation into how landslide distributions vary through time, and how this impacts landslide susceptibility. Here, we apply Kolmogorov‐Smirnoff and chi‐square statistics to a 30‐yr inventory of monsoon‐triggered landslides from Nepal to systematically quantify how landslide spatial distributions vary through time in “normal” years and years impacted by extreme events. We then develop binary logistic regression (BLR) susceptibility models for 12 yrs in our inventory with >400 landslides and use area under receiver operator curve validation to assess how well these models can hindcast landslide occurrence in other years. Landslide distributions are found to vary through time, particularly in years impacted by storms (1993 and 2002), earthquakes (2015), and floods (2017). Notably, Gorkha earthquake landscape preconditioning shifted 2015 monsoon‐triggered landslides to higher slopes, reliefs, and excess topographies. These variations significantly impact BLR susceptibility modeling, with models trained on extreme years unable to consistently hindcast landslide occurrence in other years. However, developing BLR models using increasingly long historical inventories shows that susceptibility models developed using >6–8 yrs of landslide data provide consistently good hindcasting accuracy. Overall, our results challenge time‐independent assumptions of landslide susceptibility approaches, highlighting the need for time‐dependent modeling techniques or historical inventories for landslide susceptibility modeling.