## 3 Results and Discussion
### Evaluation of GPP Based on the Modified Efficiency Index of Kling-Gupta
\begin{table}
\begin{tabular}{c c c c} \hline  & **Annual** & **Dry** & **Wet** \\ \hline Bias & 0.083 & 0.109 & 0.096 \\ Correlation & 0.254 & 0.350 & 0.358 \\ Variability & 0.131 & 0.168 & 0.227 \\ \hline \end{tabular}
\end{table} 
Table 2: Mean median KGE components scores. Bias, Correlation and Variability are expressed as \(|B-1|\), \(|R-1|\) and \(|V-1|\), respectively (see Equation (1)). Figure 5: Spatial distribution of KGE scores for GPCC025, MSWEP2, GPCC05, MSWEP, and CHELSA. Colors for KGE range from violet to red, representing very poor to optimum skill respectively. Diagrams ordered according to median annual performance (see Figure 3). rain-gauge and satellite information, we find that, at least in our study domain, PERSIANNCDR performs worse than CHIRPS. We suggest that the use of a coarse precipitation dataset (2.5\({}^{\circ}\)) such as GPCP [16], possibly reduces the skill of PERSIANNCDR to adequately represent precipitation features in the Caribbean. 2. CRU and PRECL datasets are among the station-based products with the lowest performance in representing precipitation variability across the Caribbean. This is likely due to the lower number of surface station taken into account in their development, as compared with other station based GPP such as GPCC025, GPCC05, and HERREAULT. 3. Although we expected similar performance for HERREAULT as GPCC, considering the former was built upon a GPCC product [32], this was not observed. In this regard, we found significant differences between the GPCC version 7 (used in HERREAULT) and version 8 (evaluated here), as shown by their low correlations over our study domain (Figure 6). This could in part explain the differences in the performance between HERREAULT and the GPCC products used in this paper. Nevertheless, further investigation should be carried out due to the potential to generate a more accurate regional high-resolution product (such as HERREAULT) based on the GPCC full data version 8. 1. The lowest KGE scores, suggesting a relatively poor performance, correspond to most reanalyses (except for ERA5) and consistently CPCGLOBAL. The reanalysis products are among the GPP with the lowest performance, which reflects the limitations of the current atmospheric models to represent the fine-scale precipitation processes. This might be due to limitations in representing sub-grid convection and land-surface parameterization schemes. This result also reveals a constraint related to the spatial resolution needed to adequately represent the small islands of the Lesser Antilles. A possible solution to this limitation could be the incorporation of high-resolution, non-hydrostatic, regional models [52]. 4. Although ERA5 significantly underestimates precipitation across the Caribbean, it is the reanalysis product with the best performance based on the KGE scores. This is seen in the better  
Figure 6: Correlation between full data GPCCversion 8 and version 7 over the period 1983–2000. The products were used with a native spatial resolution of \(1^{\circ}\times 1^{\circ}\). correlation and variability scores with ground stations. This suggests its potential for use in the development of new GPP products for the Caribbean especially with bias correction techniques employed--which may prove easier than trying to improve the linear correlations [27]. For example, it is hypothesized that the CHELSA product, which was developed based on ERA-Interim [44], may perform even better over the region if using ERA5 instead, which is currently the primary reanalysis model from the ECMWF.