## 1 Introduction  
Precipitation is one of the most important climatic variables and it is included in a large number of studies of weather and climate. Precipitation datasets in the form of a regular grid (herein, gridded precipitation products (GPP)) have particular importance for various applications related to climate change detection [1], climate model evaluation [2], and the parameterization of stochastic generators [3,4]. GPP are also useful for understanding the interactions between climate and terrain, as well as hydrological and biogeochemical processes [5, 6, 7]. The adequate representation of the spatial and temporal variations of precipitation is difficult, mainly because it is significantly affected by the availability and quality of observed data. Although the rain gauges are the most suitable ways to estimate precipitation [8], stations are often sparsely located, and present errors that arise from instrumental and non-instrumental problems (e.g., wind influences) which limit the quality of the observed data [9,10]. Also, despite the notable advances in estimating precipitation from satellite data, this option is limited by temporal sampling and algorithm errors [11]. Additionally, the spatial distribution of precipitation is also affected by complex topography [12]. In recent years, many GPP have been developed, covering different periods, and temporal and spatial scales. These datasets were created, not only using ground-based measurements [13,14] or satellite data [15] but also by combining different sources of precipitation data [16, 17, 18]. The accuracy of those GPP has been assessed at different spatial and temporal scales using disparate methodologies, showing the advantages and limitations of each product. Most of these assessments were conducted on global or regional scales [18,19], such as in Africa [20,21], Europe [22], Asia [23], and North America [24]. In Latin America, most evaluation studies are concentrated in portions of South America [25, 26, 27]. In general, these studies suggest different pictures of the best GPP datasets at global and/or regional scales and reinforce the need to evaluate the accuracy of a particular GPP in a region. This problem is especially critical for insular regions such as the Caribbean Islands. For the Caribbean region, where small scattered countries are dominant and a sparse land-based observational precipitation network exists, there are very few studies evaluating the accuracy of GPP. An intercomparison study using several GPP, including various models from the Coupled Model Intercomparison Project phase 3 (CMIP3) [28], highlights differences in representing the spatial pattern of rainfall in the eastern half of the Caribbean Islands. Ref. [28] concluded that no single GPP was without its discrepancy and that the high-resolution satellite products examined were reasonably representative notwithstanding their short time span (five years at the time of the study). Many of the GPP examined in that study have since been updated to incorporate more observations, improved interpolation techniques, and/or a finer resolution, suggesting the need for re-evaluation. Other studies have compared different GPP when evaluating the spatial and temporal variability of rainfall across the Caribbean [29] and the performance of climate models [30,31]. However, these studies start with the premise that the GPP offer either the best available or the most reasonable representation of the regional climate and thereafter use the GPP as the reference for analysis being undertaken. Recently, ref. [32] produced a very high-resolution monthly dataset (\(0.033^{\circ}\times 0.033^{\circ}\)) that, along with other climate variables, includes precipitation. The authors prove a better accuracy of their downscaled precipitation product, as compared with the original low-resolution (\(1^{\circ}\times 1^{\circ}\)) Global Precipitation Climatology Centre (GPCC) version 7 [14]. As suggested by [PERSON] et al.