reported by each EO product for each country. Mean estimates are generated using a fractionalpolynomial and graphs include 95% confidence intervals on the mean estimates. We again make note of several trends. One trend (T3) is that ARC2, CHIRPS, and CPC form a cluster, measuring more days without rain, relative to the other EO sources. Another trend (T4) is that the two reanalysis data sets, ERA5 and MERRA-2 form a different cluster, measuring fewer days without rain, relative to other EO sources. A final trend (T5) is that TAMSAT reports substantially fewer days without rain than the first group but substantially more than the second, ending up somewhere in the middle.

Again, there are exceptions to these trends. In Ethiopia, Nigeria, and Tanzania, T3, T4, and T5 hold. While MERRA-2 reports the fewest days without rain in all four countries, the EO product that reports the most days without rain varies from country to country. Additionally, the size of the gap between the products reporting drier conditions and TAMSAT varies, with TAMSAT reporting nearly as little rain in Tanzania as the others while it reports nearly half the number of days without rain relative to the other products in Ethiopia and Nigeria. Turning to Malawi, T4 and T5 (TAMSAT in the middle) hold, though T3 does not. Instead, CPC measures about 20 percent more days without rain than ARC2 or CHIRPS. Next, looking at Niger, T3 and T5 hold. Unlike in other countries, in Niger ERA5 does not agree with MERRA-2 but rather reports very few days without rain, similar to ARC2, CHIRPS, and CPC. Finally, in Uganda, only T4 (ERA5 and MERRA-2 cluster) holds. TAMSAT and CPC report the fewest days without rain while ARC2 and CHIRPS fall in between this high reporting cluster and the low reporting re-analysis cluster.

In Figure 6 we present the distribution of mean daily seasonal temperature (measured in<sup>_◦_</sup> C), by country and EO product. Compared to the distribution of mean daily rainfall, the figures show much tighter distributions around mean temperatures. All EO sources in all countries produce essentially the same results for temperatures. This results is our first temperature trend (T6): there is relatively low variation across EO sources within a country. This is largely due to the relatively low temperature variation in these tropical and sub-tropical countries. While there is some variation country-to-country in terms of which EO product reports the hottest average temperature, these differences are not agronomically meaningful.

Figure 7 estimates the mean GDDs in a year using a fractional-polynomial and includes 95% confidence intervals on the mean estimates. As with number of days without rain, GDD represents a relative coarsening of the data by converting measured temperature into a count variable for the number of days in which temperature fell within a given range. Compared to mean daily temperature, there is less overlap in the values each EO product produces, though relative to the rainfall measures there is again substantially less variation across temperature products. This gives us our final trend (T7): there is relatively low variation in GDD across EO sources.

Examining each country individually, we see T7 most clearly in Ethiopia and Uganda where there is overlap in the confidence intervals for all three products across almost all years. Nigeria also 13