## 3 Results and Discussion
### GPP Evaluation Based on SPI  
The results for selected drought statistics determined using SPI-0.5 and SPI-1.5 are presented in Figure 7 for SPI 3, 6, and 12. Overall, most of the GPP fail in representing accurately the SPI-0.5 statistics (Figure 7a,c), having different ratings for different statistics. The GPP with best performance were MSWEP, MSWEP2, ERA5, and CHELSA. For SPI-1.5 (Figure 7b,d), 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. The best performing products across both SPI statistics are the same as those with highest correlations in the KGE analysis (see Figure 3). We note, however, that care must be exercised when assessing GPP performance on the basis of an individual drought statistic, as it only offers a limited perspective while not capturing the overall ability of the GPP across SPI's of different intensity and length. For instance, following the methodology used by [23], who only consider DF in the evaluation of GPP, we would conclude that CRU or TERRACLIMAE are among the best GPP for SPI-0.5. Conversely, if we take into account the DE statistics, the performance of those GPP is not as good. Similarly, we find that PRECL is among the GPP with best skill in representing DE statistics for SPI-0.5, but not DF for SPI-0.5. In conducting the SPIAA analysis, we compared the SPIAA time series from observations against the GPP for SPI 3, 6, and 12. To do so, we selected \(-0.5\) and \(0.5\) as SPI thresholds to identify droughts and pluvial events. Figure 8 presents the results of SPIAA using SPI12. (Though SPI3 and SPI6 are not shown they indicate similar results). For drought conditions, the GPP have different performances across the drought categories, with correlations ranging from 0.3 to 0.8, and temporal variability greater than observations. The results are better for areas with SPI lower than \(-0.5\) (Figure 8a) and worse when moderate and severe drought categories are considered (Figure 8b,c). MSWEP, CHIRPS, and  
Figure 7: Differences between observation and the GPP for drought statistics derived from SPI-0.5 (left column) and SPI-1.5 (right column). Drought Frequency (DF) and Number of Drought Events (DE) are shown in panels (**a,b**), and (**c,d**), respectively. Bars show values for SPI3 (blue), SPI6 SPI6 (red) and SPI12 (yellow). In the case of DF (**a,b**) the differences are represented as percentage. GPCC025 show the best skill overall across SPI categories, although their accuracy for severe dry category is very low. On the other hand, it is also noted that some GPP show noticeable better skill for pluvial conditions, as reflected by a lower dispersion within each category, higher correlation scores, and standard deviation values closer to the observations (Figure 8d-f). In this regard, the GPCC025, GPCC05, and MSWEP2 products display the best performance. The aforementioned results suggest the following about the use of SPI as a performance measure. Firstly, the use of drought statistics offers only a general idea about the GPP performance, without  
Figure 8: Taylor diagrams for the areas with SPI lower than \(-0.5\) (**a**), moderate and severe droughts (**b,c**, respectively). SPI greater than \(0.5\) as well as moderate and severe wet conditions are shown in (**d–f**), respectively. reflecting possible differences in the temporal occurrence of drought events between observations and GPP. Secondly, GPP skills are seemingly better for pluvial conditions than for drought ones, at least for the time period considered in this study. Thirdly, the assessment based on SPI suggests that MSWEP2, MSWEP, and GPCC025 are among the best overall performers, even when other GPP products show a better ability for individual SPI indicators.