be used in the same application and do not produce substantially different results. Results from CHIRPS, ARC2, and TAMSAT tend to be in agreement, producing mostly positive coefficients in all countries, except Tanzania where they all produce mostly negative coefficients. CPC tends to agree with CHIRPS, ARC2, and TAMSAT, though unlike these other three products CPC produces more positive coefficients than negative coefficients in Tanzania. Less consistent, both in comparison to itself and to other remote sensing products, is MERRA-2. MERRA-2 tends to report positive and negative coefficients in equal number in some countries (Ethiopia, Niger, Tanzania) but then exclusively positive coefficients in other countries (Malawi, Nigeria, and Uganda). This inconsistency makes MERRA-2 a hard product to recommend for measuring the volume of precipitation (mean daily or total seasonal) though it does frequently, if inconsistently, agree with CHIRPS, CPC, ARC2, and TAMSAT. By comparison, ERA5 tends to be very consistent with itself across countries but is the notable outlier among the six precipitation products.

ERA5 reports more significant coefficients than the other products. No other product reports significant coefficients in more than 80% of regressions in any country, while ERA5 reports more than 80% of coefficients as significant in Ethiopia, Niger, and Nigeria. Among significant coefficients, ERA5 results in almost exclusively positive signs on mean and total rainfall, even in Tanzania where most of the other products result in negative coefficients. The one exception for ERA5 is Nigeria, where results show 80% of significant coefficients as negative, while all of the other products have a majority of coefficients as positive. Based on these results, MERRA-2 and ERA5 appear to mismeasure the volume of precipitation during the growing season. As shown in Figure 4, MERRA-2 reports substantially less rainfall while ERA5 reports substantially more rainfall than the other remote sensing sources. This appears to produce noisy or inconsistent results for mean daily and total seasonal rainfall when using MERRA-2. For ERA5, the large measured volume of precipitation appears to produce results that are in stark contrast to those results produced by other precipitation products.

Next, we consider Figures 34 and 35 which present specification charts for the number of rainy days and the share of days in the growing season that it rained. As one might expect, given the results of mean and total rain, the two rain day metrics are highly correlated at around 0 _._ 80. The except being for MERRA-2, where the correlation between the number of rainy days and the percentage of rainy days in a season is only 0 _._ 60. Despite the lower correlation within MERRA-2, we will only discuss the number of rainy days in detail.

Results for rainy days are more consistent across country and remote sensing product than mean or total rainfall. Coefficients on rainy day are almost always significant, regardless of country or of remote sensing source. The one exception is MERRA-2, with most coefficients in Ethiopia, Malawi, and Uganda being not significant while 75% of coefficients in Tanzania are significant. These results contrast with most of the other remote sensing products, which tend to report mostly significant coefficients in every country, except Tanzania.

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