# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 3 Results and Discussion
### Assessment Based on Rainy Months and Summer Months  
The current study conducted a seasonal evaluation based on the rainy and summer months. In the study region, the wet season coincides with the cold season, typically spanning from November to March. The summer season is the dry season, and summer rains, usually in the form of thunderstorms, typically account for less than 10% of the total annual precipitation. Figure 13 shows the seasonal cycles of the GPM products compared to those of rain gauges. These cycle series were obtained by taking the arithmetic average of the multi-year mean precipitation for each month across all stations. Overall, all products were able to reproduce the seasonal cycle, with better performance for the GPM-F product and difficulties for the GPM-L and GPM-E in reproducing this cycle. However, they were less accurate in describing  
Figure 12: Frequencies of the different categories of intensities for each precipitation product and each rainfall station. the magnitude of rainfall. The ERA5 product exhibited high magnitude, which explains why it consistently showed the largest biases, RMSE, and MAE errors. According to the results presented in Figure 14, all products tend to overestimate monthly precipitation from October to December, except for GPM-L, which slightly underestimates them in October. This overestimation persists for GPM-F and ERA5 during the subsequent rainy months (from January to April), while the other two GPM models (GPM-L and GPM-E) show a slight underestimation for these months. In general, ERA5 exhibits large overestimations, while the GPM products have relatively low biases. In terms of consistency, the results also revealed that the ERA5 had the highest correlation with the observed data for all rainy months, followed by GPM-F, GPM-L, and GPM-E, respectively. Similarly, in terms of the Spearman correlation coefficient, ERA5 demonstrated a better ability to capture the spatial patterns of monthly precipitation for rainy months, followed by GPM-F, GPM-L, and GPM-E. But despite its good correlations, ERA5 has the highest RMSE and MAE among the four precipitation products. The results of the RMSE and MAE also indicate that the GPM-F produced the lowest errors for the months of October to December, while from January to April, the lowest errors were obtained with the GPM-L model (Figure 14). The results displayed in Figure 15 indicate that the GPM-F product had the lowest bias in the summer months, followed by GPM-L and GPM-E. However, ERA5 presents low performance, except for the month of August where it outperforms the GPM-L and GPM-E products. The RMSE and MAE were also lower for GPM-F, followed by GPM-E, GPM-L, and ERA5, respectively. The values of Pearson and Spearman correlation were relatively low. Overall, the precipitation products exhibit low biases as well as low RMSE and MAE for the summer months compared to the rainy months. However, they show higher correlations for the rainy months than for the summer months. Figures 16 and 17 present the variation of observed average daily precipitation as well as precipitation products during the rainy and summer months from 2000 to 2020. In general, none of the evaluated models are able to satisfactorily reproduce the average daily precipitation. However, when comparing the four products, it is observed that the GPM models (especially GPM-F) exhibit better capability than ERA5. By examining the frequencies of different rainfall intensities for each month and rain gauge, similar results to those presented in Figure 12 were obtained to assess the products' ability to reflect these frequencies. The comparison revealed that the GPM products were able to successfully reproduce the frequencies of all five classes, while ERA5 tended to underestimate the lowest rainfall class (0 \(\leq\) T \(<\)1) and overestimate the other classes. Figure 13: Ability of precipitation products to describe seasonal cycles for each rain gauge. Figure 14: Boxplots of rainy month regression metrics (Red line: Median; Dots: Outliers).