EO data. Produced by the Climate Hazards Group at the University of California, Santa Barbara this dataset is designed for monitoring of drought and environmental change at a global level. To minimize latency, there are two products, a preliminary version with two day lag, and final output available at three weeks. Outputs are available at time-steps from six hours to three months. As inputs, CHIRPS makes use of a monthly climatology CHPclim, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis version 7 (TMPA 3B42 v7) and global Thermal Infrared Cold Cloud Duration (TIR CCD) from two NOAA archives. The EO data are then merged with gauge data from five public archives, including the Global Historical Climatology Network (GHCN) and GTS, several private sources, and meteorological agencies. While targeted gauge data collection efforts resulted in a greater number of input stations for years prior to 2010, the number of stations going forward is more limited, particularly in Sub-Saharan Africa. Detailed metadata by country is available and may be a useful reference to determine if coverage for a region of interest is sufficient for the analysis.

Validation for select countries found that the climatology input CHPclim outperformed other climatology datasets in data sparse regions and complex terrain (Funk et al., 2015). Further, in an evaluation of wet season statistics CHIRPS showed less bias than other rainfall sources and good correspondence with Global Precipitation Climatology Centre (GPCC) estimates.<sup>4</sup> The TAMSAT rainfall dataset is the highest spatial resolution gridded dataset used in this analysis. Inputs are similar to other merged gauge and EO products: Meteosat TIR imagery, purposefully collected archival (1983-2010) rain gauge data from meteorological agencies and other sources and GTS gauge data. Rainfall estimation is based on cold cloud duration (CCD) inferred from TIR and calibrated using gauge data within discrete calibration zones.

Validation of TAMSAT found a mean underestimation of rainfall of approximately four mm per dekad, though the bias was not always negative (Tarnavsky et al., 2014). Due to differences in methodology from CHIRPS and ARC2 precipitation products, TAMSAT is not affected by inconsistency in gauge data inputs. This makes it suitable for placing rainfall variability in the context of a long-term climatology and thus detecting unusually wet or dry conditions.<sup>5</sup> The second type of product uses assimilation models to combine a large number of observations from different sources (e.g., satellites, weather stations, ships, aircraft) to produce a model of the global climate system or a particular atmospheric phenomenon. Outputs are inferred or predicted based on the system state and understanding of interactions between model variables. EO products of this type include the European Centre for Medium-Range Weather Forecasts ERA5 and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2) (Hennermann and Berrisford, 2020; Bosilovich et al., 2016).

ERA5, based on the European forecasting model ECMWF, is one of two assimilation model

> 4Data and technical documentation are available for download from `https://data.chc.ucsb.edu/products/ CHIRPS-2.0/` .

> 5 Data and technical documentation are available for download from `http://www.tamsat.org.uk/data/` .

8