Articles | Volume 18, issue 11 
            
                
                    
            
            
            https://doi.org/10.5194/nhess-18-2825-2018
                    © Author(s) 2018. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-2825-2018
                    © Author(s) 2018. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology
                                            Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, Turin, Italy
                                        
                                    Elisa Palazzi
                                            Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, Turin, Italy
                                        
                                    Jost von Hardenberg
                                            Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, Turin, Italy
                                        
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                            Cumulative views and downloads 
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                                Total article views: 3,471 (including HTML, PDF, and XML)
                                
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16 citations as recorded by crossref.
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- Precipitation Downscaling with Gibbs Sampling: An Improved Method for Producing Realistic, Weather-Dependent, and Anisotropic Fields J. Bellier et al. 10.1175/JHM-D-20-0069.1
- Development of HYPER-P: HYdroclimatic PERformance-enhanced Precipitation at 1 km/daily over the Europe-Mediterranean region from 2007 to 2022 P. Filippucci et al. 10.5194/essd-17-5221-2025
- PreciPatch: A Dictionary-based Precipitation Downscaling Method M. Xu et al. 10.3390/rs12061030
- Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy A. Abbate et al. 10.3390/w14142218
- Response of Extreme Rainfall to Atmospheric Warming and Wetting: Implications for Hydrologic Designs Under a Changing Climate J. Zhang et al. 10.1029/2022JD038430
- Global spatio-temporal ERA5 precipitation downscaling to km and sub-hourly scale using generative AI L. Glawion et al. 10.1038/s41612-025-01103-y
- Hydrological Impacts of Large Fires and Future Climate: Modeling Approach Supported by Satellite Data C. Carvalho-Santos et al. 10.3390/rs11232832
- Mapping 30-m Resolution Bioclimatic Variables During 1991–2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches R. Wang et al. 10.1109/JSTARS.2024.3358829
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- High-resolution precipitation downscaling in mainland Southeast Asia: A novel integration of BMA and U-Net CNN T. Amnuaylojaroen 10.1016/j.envsoft.2025.106682
- CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment): a new model for geo-hydrological hazard assessment at the basin scale A. Abbate et al. 10.5194/nhess-24-501-2024
- Simulating silver fir provenance responses to climate change: A forest modelling approach in the Northern Apennines S. Oggioni et al. 10.1016/j.fecs.2025.100389
1 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 31 Oct 2025
Short summary
                    This study proposes a modification to a stochastic downscaling method for precipitation, RainFARM, to improve the representation of the statistics of the daily precipitation at fine scales (1 km) in mountain areas. This method has been demonstrated in the Alps and it has been found to reconstruct small-scale precipitation distribution. It can be employed in a number of applications, including the analysis of extreme events and their statistics and hydrometeorological hazards.
                    This study proposes a modification to a stochastic downscaling method for precipitation,...
                    
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