## 4 Conclusions
We suggest that the use of higher surface station density in these GPP highly influenced this outcome, notwithstanding that their development methodologies are different. * With respect to the evaluation based on SPI, we observe the following:
* Overall, the GPP fail in representing accurately the SPI-0.5 statistics, having different ratings for different statistics. The GPP with best performance were MSWEP, MSWEP2, ERA5, and CHELSA. For SPI-1.5, the GPP better resemble the observations with a discernible distinction between the worst and best performers, and a tendency to overestimate the maximum consecutive months with drought and the number of drought events. In general, products such as MSWEP2, ERA5, CHELSA, and MSWEP were the most accurate. * Considering the areas affected by different SPI categories, all GPP perform better for pluvial conditions than for drought for all SPI lengths. For wet periods, the correlations were higher, and the amplitudes were greater but closer to the observations. The GPCC025, MSWEP, MSWEP2, CHIRPS, and HERREAULT products had the best performance. * The strategy to explore GPP performance across varying SPI statistics as well as using the temporal variations of the areas affected by pluvial and drought conditions are complementary. This provides a more robust evaluation of the GPP and avoids potential bias in the interpretation of results. In general, we conclude that GPCC025 and MSWEP2 appear to be the best performers for the whole study domain and each sub-region as they were always among the better performing GPP across the range of metrics and evaluation methods examined. It is not a surprise that the GPP that use a greater number of ground stations in their development have a better representation of precipitation as compared to others GPP that uses much less observations. For example, in the development of GPP such GPCC, the number of stations used within our study region is relatively high, even though the number of stations has decreased in recent decades. In addition, GPCC uses many of the 60 stations we used to evaluate all GPP in this work. This fact might explain the better performance of GPCC over other station-based products. GPCC025 offers the opportunity to develop climate studies over longer time intervals because of its temporal span (1891-2017) and relatively high spatial resolution. MSWEP2 offers the possibility for studies that requires a very high spatial resolution (0.1\({}^{\circ}\)) over the last three decades. The use of other products will generally depend on the aim and particularities of their application. For example, multivariate datasets, such as TERRACLIMATE or CRU, can be considered when different climate variables are needed to calculate derived parameters, especially for very long-term assessments. At the same time, caution is recommended when employing a GPP with a relatively low performance, even if it has a high spatial resolution or shows good accuracy at global scale. As indicated earlier, whereas higher resolution is a key consideration for the Caribbean region with its small islands, it does not necessarily mean better performance, and it is still necessary to evaluate the products. A similar strategy to the methodology applied in this study can be used when assessing climate model outputs against observations, i.e., extending the analysis beyond just bias or annual cycles. Our results also indicate that revisiting previous evaluations of model performance over the region could produce new interpretations about accuracy, at least in the case of precipitation. For example, the conclusions reached by [30; 31; 46] on the accuracy of regional climate models to reproduce the observed precipitation may potentially be modified if GPCC025 is used as a reference observational dataset, instead of CRU, particularly for the Eastern Caribbean.