# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 3 Results and Discussion  
### Pixel Scale  
The results show that the accuracy values for the GPM-L and GPM-F models are 0.75 and 0.74, respectively, indicating relatively good performance. However, the ERA5 model has a lower accuracy value of 0.46. For the precision, GPM-F and GPM-L have median values of 0.38, while GPM-E has a precision value of 0.37. The ERA5 model has a lower value of 0.22, indicating a relatively low ability to predict positive data. Overall, these precision values suggest that the GPM products have a slightly better performance than ERA5 in terms of predicting positive data, but all models may need improvements to be more accurate (Figure 3). Concerning recall values, the median for GPM-F and GPM-L is 0.81, while GPM-E has a value of 0.80. The ERA5 model has a higher recall value of 0.98, indicating a better ability to detect positive data, but at the cost of a higher number of false alarms. In general, high recall values indicate a good ability to detect positive data. However, it is important to consider recall in conjunction with precision to obtain a complete picture of the model's performance. In this regard, the F1-score combines precision and recall to evaluate the model's binary classification performance. The median F1-score for GPM-F and GPM-L is 0.52, while GPM-E has an F1-score of 0.50. The ERA5 model has a lower F1-score of 0.36. The AUC-ROC values indicate the model's ability to distinguish between positive and negative classes. The GPM-F and GPM-L products have AUC-ROC values of 0.78, while GPM-E has a value of 0.77. The ERA5 model has a lower AUC-ROC value of 0.67, suggesting the lowest performance in this aspect. For the false discovery rate (FDR), which evaluates the proportion of errors among positive predictions, GPM-F and GPM-L have an FDR of 0.62, while GPM-E has a value of 0.63. The ERA5 model has a higher FDR of 0.78, indicating a higher rate of false positive predictions. Overall, and in conclusion, the GPM products generally perform better than the ERA5 model in terms of accuracy, precision, F1-score, AUC-ROC, and FDR (Figure 3). The good performance of the GPM products is probably due to its relatively high spatial resolution compared to the ERA5 reanalysis product. Figure 2: Training and testing in the neural network context (“?” means the predicted value). The regression metrics indicate that at all temporal scales (daily, monthly, and annual), the GPM products (GPM-F, GPM-L, and GPM-E) generally exhibit lower bias compared to the ERA5. The latter tends to overestimate precipitation, while the GPM-F consistently demonstrates the lowest bias among the GPM products. Regarding correlation, the results show that the GPM products have improved correlations with observed data at the monthly and annual scales compared to the daily scale. At this daily scale, the ERA5 product exhibits the highest correlation, followed by GPM-F, GPM-L, and GPM-E. However, on the monthly scale, GPM-F shows the highest correlation, followed by ERA5, GPM-L, and GPM-E. The correlation improvement is more significant at the monthly scale than at the annual scale (Figure 4). In terms of spatial consistency, the ERA5 and GPM-F products exhibit the most consistent spatial patterns with the station data across all temporal scales. On the daily scale, ERA5 performs better than GPM-F, while on the monthly and annual scales, GPM-F  
Figure 4: Boxplots of regression metrics on daily, monthly, and annual time scales of IMERG and ERA5 precipitation products (Red line: Median; Dots: Outliers). Figure 3: Boxplots of classification metrics at the daily scale of IMERG and ERA5 precipitation products (Red line: Median; Dots: Outliers). outperforms ERA5. GPM-L and GPM-E also show relatively good performance at the monthly and annual scales but weaker performance at the daily scale. Ref. [17] also obtained similar results in their evaluation of IMERG and ERA5 precipitation products' performance over the Mongolian Plateau.