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Climate change impacts on sediment production and transfer processes on hillslopes and through channels are governed by possible changes in precipitation, runoff, and air temperature. These hydrological and geomorphological impacts are difficult to predict in temperature‐sensitive Alpine environments. In this study, we combined a stochastic weather generator model with the most current climate change projections to feed a hillslope‐channel sediment cascade model for a major debris‐flow system in the Swiss Alps (the Illgraben). This allowed us to quantify climate change impacts and their uncertainties on sediment yield and the number of debris flows at hourly temporal resolution. We show that projected changes in precipitation and air temperature lead to a reduction in both sediment yield (−48%) and debris‐flow occurrence (−23%). This change is caused by a decrease in sediment supply from hillslopes, which is driven by frost‐weathering. Additionally, we conduct model experiments that show the sensitivity of projected changes in sediment yield and debris‐flow hazard to basin elevation, with important implications for assessing natural hazards and risks in mountain environments. Future changes in hydrological and sediment fluxes are characterized by high uncertainty, mainly due to irreducible internal climate variability. Therefore, this stochastic uncertainty needs to be considered in climate change impact assessments for geomorphic systems. Key Points: A chain of climate‐hydrology‐geomorphology models is used to quantify possible impacts of climate change on sediment yields and debris flowsFuture climate conditions favor increases in sediment‐transport capacity but a reduction in sediment supply limits debris‐flow activityA reduction in sediment yield of −48% is expected by 2085; predicted reductions in nearer future are within present‐day natural variability [ABSTRACT FROM AUTHOR]

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications. [ABSTRACT FROM AUTHOR]