## 3 Results and Discussion
### Evaluation of GPP Based on the Modified Efficiency Index of Kling-Gupta  
Figure 3 shows the results from the modified KGE analysis and its components (Bias, Correlation and Variability). The five GPP with highest KGE median values are the two versions of GPCC (GPCC025 and GPCC05), the two MSWEP products (MSWEP and MSWEP2) (all greater than 0.7) and the CHELSA product (median score of 0.68). The GPP with the lowest median KGE was CPCGLOBAL (<0.4) which also had large deviations from observations in the climatological analysis. The same GPP emerge as the best performers with respect to median KGE for the wet and dry seasons (Figure 4), though their relative rankings shift depending on season. Across the two seasons, CPCGLOBAL remains the worst performer. The five best GPP generally perform well across the entire Caribbean,  
Figure 2: Annual cycle of observations and GPP, averaged over the region (**a**), western subregion (**b**) and eastern subregion (**c**) defined in Figure 1.  
with high values of KGE distributed across the region (Figure 5). The inclusion of a higher number of rain gauges and other improvements in the 8 th version of GPCC [40] might explain the good skill of GPCC025 and GPCC05. The MSWEP and MSWEP2 products likely also perform well because of the use of a high-resolution dataset based on surface stations. Furthermore, since CHELSA uses a similar downscaling procedure as GPCC025, it shares similarities with GPCC in our study region. Figure 4: As in Figure 3 but just for KGE index for dry (left) and wet (right) seasons. Figure 3: Boxplot of the _KGE_ index and its components \(B\) (bias), \(R\) (correlation) and \(V\) (variability). The names of the GPP are colored according to data source considered in its development (red: stations; brown: stations and reanalysis; blue: stations and satellites; orange: reanalysis and green: multisources). The GPP have been sorted according to the median value of KGE index. In all the cases the optimal value is 1.0. Figure 3 also shows the annual KGE component scores for each GPP. Relative performance with respect to correlation generally matches that for the overall KGE score suggesting the strong influence of the latter in the calculation of the former. It is noted that the five best GPP and the worst GPP with respect to median KGE also emerge as the GPP with highest correlation values (ranging from 0.752 to 0.841) and lowest value (0.582) respectively. A similar result was found by [24] when evaluating various daily precipitation datasets over the conterminous US. As in [24], we confirm that correlation has a dominant influence on KGE scores. For GPP to have a better representation of observations, then, improving this statistic, without degrading the information content, should be a priority. When the analysis is done by season (not shown), the median values of temporal correlations are comparable, ranging from 0.5 to 0.8 (0.45 to 0.75) for the dry (wet) season. Almost all GPP underestimate the rainfall variability in the region (median values from 0.7 to 0.95), except for CPCGLOBAL and MSWEP2, which are the only products that overestimate it. Across individual seasons MSWEP2 also shows the best results for variability (not shown). Table 2 presents the mean scores across all GPP for the median KGE's components. Table 2 highlights the poor performance of correlation in general, which greatly influences the final KGE scores. Table 2 also suggests that the variability component should also be taken into account to improve the GPP performance in the wet season. We also note the following based on the calculated scores for KGE and its components:  
* Based on the KGE index, GPCC025 and GPCC05 (gauge only), MSWEP and MSWEP2 (gauge, satellite, and reanalysis), and CHELSA (gauge and reanalysis) display an overall good performance, including for each season and the whole year. Of the two datasets that merge