in maximum value of up to 18 feet.

Figure 5 further explores these differences by estimating the mean number of days without rain reported by each remote sensing product in each season. Mean estimates are generated using a fractional-polynomial and graphs include 95% confidence intervals on the mean estimates. CHIRPS, CPC, and ARC2 frequently report a similar number of days without rain, as do MERRA-2 and ERA5. TAMSAT is often similar to CPC and ARC2, though sometimes deviates from the other remote sensing products. Typically, the measurements from CHIRPS, CPC, ARC2, and TAMSAT suggest that there are more days without rain, relative to the measurements from MERRA-2 and ERA5. Unlike total seasonal rainfall, where we saw differences in accuracy based on warm or cool regions, for days without rain we see the largest differences in accuracy between semiarid sub-regions and the sub-humid and humid sub-regions. For tropic-cool/semi-arid and tropicwarm/semi-arid, MERRA-2 and ERA5 report about 50% fewer days without rain relative to the other three products (about 120 versus 60 days in tropic-cool/semi-arid and about 200 versus 100 days in tropic-warm/semi-arid). For the four sub-humid and humid zones, MERRA-2 and ERA5 report about 70% fewer days without rain, meaning the other products are reporting three times as many days without rain. As an example, in tropic-cool/sub-humid regions, MERRA-2 reports an average of only 33 days without rain while ARC2 reports an average of 107 days without rain.

In Figure 6 we present the distribution of mean season temperature (measured in degrees Celsius), by remote sensing product and agro-ecological zone. Compared to the distribution of total seasonal rainfall, the figures show much tighter distributions around average temperature. This is especially true in tropic-warm/semi-arid and tropic-cool/sub-humid agro-ecological zones. While there is variation in the distributions, the three temperature products report very similar means, minimums, and maximums. Values are always within two degrees of each other and in the vast majority of cases they vary by less than a degree.

In Figure 7 one can see that there is more variation in reported temperatures by remote sensing product when we calculate growing degree days (GDDs). But even here, the differences are not as substantial as was seen in terms of rainfall. In the three tropic-cool sub-regions, all three temperature products report essentially the same number of GDDs. In the tropic-warm sub-regions predicted mean GDDs do differ significantly, with CPC consistently reporting fewer GDDs than the other two products. However, these differences may not be agronomically meaningful. Mean GDDs differ by about 10 days out of around 180 days in tropic-warm zones.

Summarizing the descriptive evidence, there is clearly more mismeasurement of rainfall than of temperature. Given that the amount of precipitation and the temperature in a specific location on a specific day is an objective fact, any difference in reported values is evidence of mismeasurement. We lack documentation of what the true rainfall and temperature values are for any of the households in our data, so we cannot determine which remote sensing product is wrong or which product is less accurate or which product contains greater measurement error. But what we can describe is 15