Generation of Ensemble Climate Information Under Global Warming Based on Information and Physical Law Conservation: Application to Nonstationary Processes

Key Points

• The aim is to create ensemble climate data that accurately reflects the intensity and occurrence of extreme events under global warming.
• Constructed a method for hydrological time series using the preservation of average information and physical occurrence frequency of extremes.
• Applied the method to real river basins to create time series of annual maximum rainfall.
• Quantified future changes in planned probability-scale rainfall through continuous time series predictions.
• Successfully derived extreme hydrological time series from ensemble climate data.
• Demonstrated fit of the generated time series to nonstationary probability processes based on temporal experiments.