| 26 Jun 2026
Virtual Rain: A Unified Toolkit for High-Resolution Rainfall Simulation and Disaggregation
Abstract. Stochastic simulation models are essential for investigating hydrological processes and supporting water resource management. In this study, we introduce Virtual Rain, a two-step toolkit that first generates synthetic daily rainfall time series and then disaggregates them to a user-defined temporal resolution. The toolkit builds on the approaches recently proposed by some of the authors, ensuring a realistic representation of rainfall dynamics while preserving key statistical properties. The framework is implemented through a set of Python and R routines designed to facilitate practical application. In addition to providing observed rainfall time series and the scaling exponent š¯‘› (Intensity–Duration–Frequency slope), users can configure key modelling components, including the marginal distribution, autocorrelation structure, number of lags, and target temporal resolution. The routines generate both graphical and quantitative outputs, enabling direct comparison between observed and simulated series. Virtual Rain performance is evaluated through a real-world case study, demonstrating satisfactory accuracy and highlighting the robustness, flexibility, and transferability of the proposed toolbox. The toolkit is also available through an interactive web-based platform, facilitating its use by a broad range of users.
Virtual Rain: A Unified Toolkit for High-Resolution Rainfall Simulation and Disaggregation
Authors: Cappelli Francesco, Salvatore Grimaldi, Andrea Petroselli, Emanuele Santinami
Comments to Authors
The manuscript introduces Virtual Rain, a two-step toolkit that: a) generates synthetic daily rainfall time series, and b) disaggregates them to finer temporal resolution (defined by the user). The framework is implemented through a set of Python and R routines designed to facilitate practical application. The toolkit is also available through an interactive web-based platform, facilitating its use by a broad range of users. The Authors illustrate and evaluate Virtual Rain performance through a real-world case study.
After careful reading of the manuscript, I recommend that the Authors provide some clarifications, as follows:
Comments:
Routine M1.2 is used to identify the probability distribution that best describes rainfall intensities in each season. If I understand correctly, the routine provides as output the estimated parameters for all candidate distributions. I think it will be helpful for the user to get as output, also, the name of the best distribution model.
Maybe I miss something here, but I think it would be helpful for the reader to have a clarifying note on the specific type of outputs produced by resM1.2 and resM1. 3 (lines 183-184), which are used as inputs for routine M1.4 (e.g. parameters of the best fitted distribution model, autocorrelation function).
In line 240, is there a minimum threshold for the length of annual maxima below which the model cannot be used?
In line 245, the Authors state that the a-coefficients are associated with return periods ranging from 2 to 999 yr. Is there a particular limitation or practical reason that this range is used?
In line 249, how the a-coefficients relate to the inputs of routine M2.2 in lines 254-256?
In line 260, please state the specific output/s used.
In line 261, please provide a typical range of admissible values.
In line 283, the Authors mention that there is an option for the user to determine whether the routine will preserve daily rainfall totals. For disaggregation purposes, is it technically acceptable that the simulated daily rainfall totals are not preserved at finer temporal resolutions? Please clarify.
In Figure 6 (upper panel), which type of marginal distribution is eventually used, as the reproduction of the ACS is highly affected by its selection. Also, is it technically acceptable that ACS preservation depends on the type of marginal distribution selected? Also, please note that the B autocorrelation function corresponding to Burr XII distribution, does not match the empirical one. Please provide some explanation.Ā
The model tends to overestimate the observed rainfall statistics (see Figure 7). Is this fact related to some BIAS issue? Please clarify.
Also, it seems that in August the monthly simulated rainfall tends to be larger than September and almost equal to October (see Figure 9). Any particular reason for this? Please clarify.
Recommendation:
Based on the above, I recommend that the manuscript is accepted for publication after minor revisions.