“…Synthetic series are used for different purposes, e.g., planning and design, deriving long extended time series or regionalization of rainfall to areas without measurements. Among the existing precipitation models, the following types can be found: data driven generators (e.g., resampling [1,2] and disaggregation [3][4][5]), time series models (e.g., Markov chains [6]), and event-based methods [7][8][9], such as point and cluster process-based (e.g., Neyman-Scott [10][11][12][13], Barlett-Lewis [14][15][16][17][18][19][20]) or alternating renewal models [21][22][23][24].…”