## 4 Results
The Dunn test computed \(p\)-values, reported in Table 4, indicated two distinct groups of AutoML models in terms of median performance: Indeed, the first one consisting of AutoKeras, H2O AutoML, and auto-skleam, generally showed no statistically significant difference in median performance among themselves across all the employed metrics, as indicated by their respective \(p\)-values close to \(p=1.00\); The second one, including MLBox and TPOT, also showed no significant difference in median performance between them. However, the two latter models showed significant differences when compared to the ones included in the first group across all the metrics. The last point is due to the \(p\)-values less than \(\alpha=0.05\) when comparing MLBox and TPOT with the models in the first group. Upon analyzing the AutoML models performance results displayed in Figure 6, the above-noted statistically significant differences in the computed \(p\)-values are reflected in the reported performance metrics. Indeed, auto-skleam consistently achieves lower median MAE, RMSE, and BIAS across different features compared to MLBox and TPOT for each of the considered time-lags. Moreover, auto-skleam gained NSEI and KGEI median values closer to 1.00 with respect to MLBox and TPOT. Similarly, H2O AutoML and AutoKeras also tended to perform better in terms of all the employed performance metrics with respect to MLBox and TPOT. On the other hand, the two latter AutoML models still showed competitive performance, particularly in terms of the NSEI and KGEI metrics, where they all reached median performance above the value of 0.75, which still suggests high-quality model predictions, according to the categorization reported in the Section 3.4. Regarding the results shown in Figure 7, part of the statistical tests previously applied was used to understand: (1) if there were any statistically significant differences among the employed input features regarding the test set performance; (2) If the answer to the first point was positive, which specific feature sets allowed to gain statistically significantly different forecasting performance with respect to other ones. Tests were performed with test groups defined by grouping the obtained model results by feature kind and performance metric. The Shapiro-Wilk test and Fligner-Killeen test were computed over results to check the normality and homogeneity of variance assumptions. First, the Shapiro-Wilk test results indicated that the null hypothesis of normality was rejected for most of the employed features at a significance level of \(\alpha=0.05\). In particular, for MAE and Bias, the null hypothesis was rejected for all the employed features. For RMSE, all the features rejected the null hypothesis except PERSIANN-CCS (\(p=0.088\)). For NSEI, PDIR-Now (with \(p=0.224\)), PERSIANN (\(p=0.094\)), and PERSIANN-CDR (\(p=0.082\)) did not reject the null hypothesis. Lastly, for KGEI, PERSIANN (\(p=0.103\)) and Thiessen (\(p=0.062\)) did not reject the null hypothesis. As a result, the normality assumption was not guaranteed within 19 out of 25 (the 76%) of the analyzed groups. Next, the Fligner-Killeen test was applied to check the homogeneity of variances among groups. In particular, a different Fligner-Killeen test was performed for each