## 3 Results
### Preprocessing of the Weather Information  
The analysis of historical weather data allowed us to establish the baseline in terms of both annual aggregated precipitation and storm events. Figure 7 shows the calculated annual aggregated volumes and statistical distributions of the storm events, while Table 3 summarises the storm characteristics derived from the 30 min rainfall data. To derive representative storm events in each future year, we derived two representative storm events from the historical distributions summarised in Table 3: an extreme storm with a volume of 422 mm and a duration of 2 h (85 th quantile of storm duration) and an average storm with a rainfall of 2.84 mm and a duration of 1.18 h. These values were used as a representative baseline. With these values as a representative baseline, we generated a set of two potentially representative storms for each future year (between 2025 and 2100) by simply updating the volumes according to the expected rates of change of the simulated precipitation, i.e., the rates for updating the maximum and average storm events were \(rateMaxt=\left(MaxPt-MaxPt-1\right)/MaxPt-1\) or \(rateMedt=\left(MedPt-MedPt-1\right)/MedPt-1\). Using these estimated volumes as an average and each annual variance in the data, we randomly generated a set of 1000 storms to simulate the potential evolution of storm events over the years. Table 4 summarises the statistical distributions and Figure 8 the statistical distributions of the two extreme representative scenarios. Once the simulations had been run, we were able to compare them with the baseline scenario, both in terms of aggregated annual precipitation and individual storm events, as shown in Figure 9. \begin{table}
\begin{tabular}{l c} \hline
**Category** & **Class 3** \\ \hline Urban (fully developed urban areas) (CN = 95–98) & 1.1.1, 1.1.2, 1.2.1, 1.2.2, 1.2.4 \\ \hline Protected (environmentally protected) (CN = 80–85) & 2.4.3, 3.1.1, 3.1.2, 3.1.3, 3.2.1, 3.2.3, 3.2.4 \\ \hline Other (agricultural, unused or similar) (CN = 80–90) & 1.3.1, 1.3.3, 2.1.1, 2.2.1, 2.2, 2.2, 2.3, 2.3.1, 2.4.1, 2.4.2 \\ \hline \end{tabular}
\end{table} 
Table 1: Land use groups according to the CORINE Land Cover (CLC) programme [40]. \begin{table}
\begin{tabular}{c c} \hline
**Catchment Name** & **Calculated CN Average Number** \\ \hline West & 84.53 \\ Midwest 1 & 82.60 \\ Midwest 1 tributary 1 & 85.78 \\ Midwest 1 tributary 2 & 82.86 \\ Mideast 1 & 86.05 \\ Mideast 1 Tributary 1 & 85.37 \\ Mideast 2 & 85.21 \\ East & 82.68 \\ \hline \end{tabular}
\end{table} 
Table 2: Estimated average curve numbers. Figure 7: Aggregated annual precipitation and storm statistical distributions. (**a**) shows the aggregated annual precipitation (in mm) over the past 14 years. The dashed blue line represents the annual mean. (**b**) displays boxplots of the characteristics of rainfall events—duration, volume, maximum and average intensity—with outliers excluded. The orange lines represent the medians, while the green triangles indicate the means. (**c**) shows the same rainfall characteristics, but this time including statistical outliers, which are shown as black circles. Figure 8: _Cont_. \begin{table}
\begin{tabular}{l l} \hline \hline Scenario &
\begin{tabular}{l} _Yearly distribution_ \\ (from t = 0 in 2025 to t = 76 in 2100) \\ \end{tabular} \\ \hline \multirow{2}{*}{Volume average} & \(N\) (_n = 2.84 \(\times\) rateMedt_, \(\sigma=\sigma\) t) \\ \cline{2-2}  & Storm duration = 1.18 h \\ \hline \multirow{2}{*}{Volume maximum storm} & \(N\) (_n = 422 \(\times\) rateMaxt_, \(\sigma=\sigma\) t) \\ \cline{2-2}  & Storm duration = 5.5 h \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation scenarios. Figure 8: \(Cont.\)  
Figure 8: (**a,c**) show the maximum rainfall volume per event (in mm) from 2025 to 2100 under the SSP1-1.9 and SSP5-8.5 scenarios, respectively, excluding statistical outliers. Red dots mark annual maxima, connected by dashed red lines. The dashed blue line marks the mean of annual maximum volumes.