AbstractRainfall is considered the most important physical process for landslide triggering in Portugal. It is expected that changes in the precipitation regimes in the region, as a direct consequence of climate change, will have influence in the occurrence of extreme rainfall events that will be more frequently, throughout the century. The aim of this study relied on the assessment of the projected future changes in the extreme precipitation over Portugal mainland and quantifying the correlation between extreme rainfall events and landslide events through Rainfall Triggering Thresholds (RTTs). This methodology was applied for two specific locations within two Portuguese areas of great geomorphological interest. To analyze the past frequency of landslide events, we resorted to the DISASTER database. To evaluate the possible projected changes in the extreme precipitation, we used the Iberia02 dataset and the EURO-CORDEX models’ runs at a 0.11° spatial resolution. It was analyzed the models’ performance to simulate extreme values in the precipitation series. The simulated precipitation relied on RCM-GCM models’ runs, from EURO-CORDEX, and a multimodel ensemble mean. The extreme precipitation assessment relied on the values associated to the highest percentiles, and to the values associated to the RTTs’ percentiles. To evaluate the possible future changes of the precipitation series, both at the most representative percentiles and RTTs’ percentiles, a comparison was made between the simulated values from EURO-CORDEX historical runs (1971–2000) and the simulated values from EURO-CORDEX future runs (2071–2100), considering two concentration scenarios: RCP 4.5 and RCP 8.5. In the models’ performance, the multimodel ensemble mean appeared to be within the best representing models. As for the projected changes in the extreme precipitation for the end of the century, when following the RCP 4.5 scenario, most models projected an increase in the extreme values, whereas, when following the RCP 8.5 scenario, most models projected a decrease in the extreme values.