Slow-moving landslides are hydrologically driven. Yet, landslide sensitivity to precipitation, and in particular, precipitation extremes, is difficult to constrain because landslides occur under diverse hydroclimatological conditions. Here we use standardized open-access satellite radar interferometry data to quantify the sensitivity of 38 landslides to both a record drought and extreme rainfall that occurred in California between 2015 and 2020. These landslides are hosted in similar rock types, but span more than ∼2 m/yr in mean annual rainfall. Despite the large differences in hydroclimate, we found these landslides exhibited surprisingly similar behaviors and hydrologic sensitivity, which was characterized by faster (slower) than average velocities during wetter (drier) than average years, once the impact of the drought diminished. Our findings may be representative of future landslide behaviors in California where precipitation extremes are predicted to become more frequent with climate change. Plain Language Summary Landslides are often triggered by precipitation and as a result are sensitive to local climate conditions. Climate change is impacting precipitation patterns worldwide and therefore will likely have a profound influence on landslide activity over the coming decades. Here we use standardized open-access satellite radar data to assess landslide sensitivity to precipitation across a large rainfall gradient in California between 2015 and 2020. During this time period, our study area experienced some of the wettest and driest years on record, which is a precipitation pattern that is predicted to become the norm over the next century in California. We found that landslides in both wet regions of northwestern California and dry regions of southwestern California were similarly sensitive to seasonal and multi-year changes in precipitation. These landslides moved faster than average during wet years and slower than average during dry years. Our findings further confirm landslide sensitivity to climate change under diverse hydroclimate conditions and highlight the need to establish a long time series of landslide behaviors that can be used to better predict future landslide activity.