## Materials and Methods
### AutoML Models Description and Experimental Settings  
With the aim of carrying out a comprehensive study, we selected five AutoML models based on different conceptual design choices, including auto-sklearn [50], Tree-based Pipeline Optimization Tool (TPOT) [55], H2O AutoML [56], AutoKeras [57], and ML-Box [58]. All the listed AutoML models will be introduced in the next paragraphs of the present subsection. Despite the differences in their design principles, all the employed AutoML models automated the process of training and testing a large selection of optimal candidate ML models, along with the tuning of their respective hyperparameters. All the selected AutoML models were tested relying on the Python programming language (release 3.12.0), executed on Microsoft(r) Windows(r) 11 Enterprise operating system. The code to execute the employed AutoML models was not developed from scratch, but the already available Python libraries provided by the developers of the models were leveraged for each of them. The references to each of the latter Python libraries are, respectively, provided within each reported publication [50, 55, 56, 57, 58]. The workstation used for running the experiments was equipped with a 12 th generation Intel(r) Core(tm)19-12900 processor (with 5.10 GHz maximum clock frequency, 16 cores, and 24 threads) and 64 GB of RAM (of type DDR5--4400 MHz). The auto-sklearn AutoML model is built over the scikit-learn library [59], developed in the Python programming language [50]. It employs 15 ML models, 4 data preprocessing techniques, and 14 feature preprocessing methods, thus resulting in a vast hypothesis space. Within such space, auto-sklearn formulates a Combined Algorithm Selection and Hyperparameter (CASH) optimization problem and employs Bayesian optimization to solve such a problem, aiming to find the optimal performing ML pipeline. The TPOT AutoML model is designed to automatically build and optimize ML pipelines using the well-established evolutionary computation method known as Genetic Programming (GP) [55]: at the start of each TPOT run, a fixed number of pipelines is created to form what is commonly referred to in GP as a \"population\". Then, GP is employed to evolve the latter set of pipelines that have been applied to the data, i.e., the population, and a subset of these is preserved based on their predictive performance. Finally, the highest-performing pipeline is kept either when TPOT achieves convergence or after a specified number of TPOT runs, as defined in advance by the researcher. H2O AutoML is designed to simplify the process of automatically training and testing a wide ensemble of ML models [56]. First, the H2O model applies data preprocessing steps, such as imputation, one-hot encoding, and standardization. Then, the H2O AutoML trains a random grid of several ML algorithms, for instance including Gradient Boosting Machines (GBMs), Deep Neural Networks (DNNs), and Generalized Linear Models (GLMs), relying on a carefully selected hyperparameter space. The individual models are then optimized through cross-validation to ensure the highest predictive performance. Additionally, the H2O model trains two stacked ensembles of models: the first one incorporates all the \begin{table}
\begin{tabular}{l c l l l} \hline \hline
**Product** & **Spatial Resolution (\({}^{\circ}\))** & **Selected Data Period** & **Update Frequency** &
\begin{tabular}{l} **Number of** \\ **Computed Samples** \\ \end{tabular} \\ \hline PERSIANN & \(0.25^{\circ}\times 0.25^{\circ}\) & March 2000–November 2023 & Every 2 days & 8640 samples \\ PERSIANN-CCS & \(0.04^{\circ}\times 0.04^{\circ}\) & January 2003–November 2023 & Real-time & 7980 samples \\ PERSIANN-CDR & \(0.25^{\circ}\times 0.25^{\circ}\) & January 1985–September 2020 & No longer updated & 13,023 samples \\ PDIR-Now & \(0.04^{\circ}\times 0.04^{\circ}\) & March 2000–November 2023 & Every 15–60 min & 8640 samples \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The table reports the exploited rainfall datasets estimated through remote sensing [41, 42].