# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 3 Results and Discussion
### Evolution of Metrics According to Altitude  
The analysis of the effect of altitude on the performance of the GPPs reveals interesting findings. It should be noted that altitudes in the study area range from 11 to 1624 m, and the rain gauge altitudes vary between 67 and 687 m. Therefore, there is no clear impact of altitude on the classification metrics (Figure 8). However, when considering the regression metrics based on altitude, the ERA5 product showed a notable increase in bias at higher altitudes (Figure 9). ERA5 tends to overestimate observed precipitation in high-altitude  
Figure 6: Linear regression between the monthly precipitation observed by the rain gauges and that estimated by the precipitation products IMERG and ERA5.  
Figure 7: Linear regression between the annual precipitation observed by the rain gauges and that estimated by the precipitation products IMERG and ERA5.  
stations and exhibits a moderate overestimation in low-altitude stations. This suggests that the ERA5 product is more sensitive to altitude, leading to deviations in precipitation estimates at different altitudes. On the other hand, the GPM products (GPM-F, GPM-L, and GPM-E) do not show a significant effect based on altitude. This implies that the GPM satellite products are less influenced by altitude variations in the study area.  
The overestimation of precipitation in the ERA5 and satellite products can be attributed to several factors, as supported by various studies: (a) Dynamic and thermodynamic processes: [PERSON] et al. (2023) [79] suggest that ERA5 overestimates precipitation due to the overestimation of summer winds in the Tibetan Plateau region. [PERSON] et al. (2021) [80] propose a thermodynamic bias, as ERA5 overestimates atmospheric humidity in Iran, leading to an overestimation of precipitation. [PERSON] et al. (2021) [81] found that dynamic processes such as convection and vertical air movements, along with a thermodynamic bias from overestimated humidity, contribute to the overestimation of ERA5 precipitation in the Tibetan Plateau region. (b) Spatial resolution: [PERSON] et al. (2019) [82] highlight that  
Figure 8: Evolution of classification metrics as a function of altitude.  
Figure 9: Evolution of the regression metrics as a function of altitude for the daily, monthly, and annual scales (Blue line: Regression line; Blue area: Confidence interval).  
the spatial resolution of climate models, including ERA5, can contribute to the overestimation of precipitation. [PERSON] et al. (2020) [PERSON] et al. (2020) found an overestimation of precipitation in ERA5 over mainland China due to its low spatial resolution. (c) Representation of cloud and precipitation processes: [PERSON] et al. (2020) [PERSON] et al. (2020) demonstrate that ERA5 overestimates precipitation over mainland China due to an inadequate representation of cloud and precipitation processes, particularly an overestimation of high-intensity events. (d) Atmospheric aerosols: [PERSON] et al. (2013) [PERSON] et al. (2013) show that atmospheric aerosols can modify precipitation distribution and lead to an overestimation of precipitation in high-altitude areas. The results also indicate increases in both MAE and RMSE from low to high altitudes at all scales. Furthermore, the Pearson and Spearman correlation coefficients decreased as altitude increased, for both ERA5 and GPM products, with a particularly pronounced decrease at the daily scale for the ERA5 (Figure 9).