## 2 Data and Methods
### Methodology  
We followed a point-to-grid approach by extracting the GPP grid cell over the underlying weather stations, which produces similar results as the neighborhood interpolation method. Features of precipitation at both point and spatial scale are analyzed over the subregions shown in Figure 1. We evaluate the GPP following 2 steps. First, we assess the ability of each GPP to represent the main features of the rainfall annual cycle in our study domain, including the presence of the MSD. Secondly, we use indicators of performance such as the modified efficiency index of Kling-Gupta (KGE) [47] and the standardized precipitation index (SPI) at 3, 6, and 12 months [48; 49; 50]. Since the KGE index integrates other statistical metrics such as the bias and correlation coefficients, we also use these metrics in our evaluation. The KGE is calculated for every location (station/grid point) using monthly precipitation during the dry (November-April) and wet (May-October) periods and annually with the following equation:  
\[KGE\ =\ 1-\sqrt{\left(R-1\right)^{2}+\left(B-1\right)^{2}+\left(V-1\right)^{2}} \tag{1}\]  
\[R\ =\ \frac{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)\left(S_{i}-\overline{ S}\right)}{\sqrt{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)}\sqrt{\sum_{i=1}^{n} \left(S_{i}-\overline{S}\right)}} \tag{2}\]  
\[B\ =\ \frac{\overline{S}}{O} \tag{3}\]  
\[V\ =\ \frac{CV_{s}}{CV_{o}} \tag{4}\]  
where \(n\) is the number of observations, \(O_{i}\) and \(S_{i}\) are the observed and estimated data, respectively, in month \(i\); \(O\) and \(S\) are the average values of the observations and the estimates, respectively.  
The optimal value of the \(KGE\) index and its components (\(R\), \(B\), and \(V\)) is equal to one. \(R\) is the linear correlation between observations and precipitation estimates; \(B\) is the bias, which reflects when the estimated values are greater or lower than observations, while \(V\) is similar to the bias but for variability.  
We calculate SPIs for 3, 6, and 12 months to explore how each GPP represents the observed statistics of drought and pluvial conditions (SPI \(\leq\) -1.0 and SPI \(\geq\) 1.0, respectively) [48] in the Caribbean. The evaluation considers both the drought statistics as well as the number of grid points within the region with different SPI categories. We evaluate drought statistics such as the frequency of dry months (DF) and the number of drought events (DE). We define a drought considering the experiences from the Meteorological Service of Cuba, in which a drought occurs when the SPI value of a single or sequence of months falls below the \(-\)0.5 threshold (herein SPI-0.5). A dry interval ends when the SPI becomes greater than \(-\)0.5. Similarly, an SPI value of \(-\)1.5 (SPI-1.5) is also used to examine severe droughts. The number of the grid points either under pluvial or drought conditions is determined by creating a time series of the number of stations or GPP grid cells affected by each category. Since this time series  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No** & **Abbreviation** & **Sources** & **Full Name** & \
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Temporal** \\ **Coverage** \\ \end{tabular} & **References** \\ \hline
14 & JRA55 & R &
\begin{tabular}{c} Japanese 55-year Reanalysis \\ (JRA-55) \\ \end{tabular} & 0.56\({}^{\circ}\) & 1959–present & [46] \\ \hline
15 & MSWEP & G,S,R &
\begin{tabular}{c} Multi-Source \\ Weighted-Ensemble \\ Precipitation (MSWEP) version \\ \end{tabular} & 0.25\({}^{\circ}\) & 1979–2015 & [18] \\ \hline
16 & MSWEP2 & G,S,R &
\begin{tabular}{c} Multi-Source \\ Weighted-Ensemble \\ Precipitation (MSWEP) version \\ \end{tabular} & 0.1\({}^{\circ}\) & 1979–2015 & [24] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._is an indicator of the pluvial/drought area, we called it the SPI Areal Assessment (SPIAA). We use the CARiDRO system [51] to derive most of the hydroclimate statistics and calculations.