of papers use ARC2, including Arslan et al. (2015), Amare et al. (2018), Asfaw and Maggio (2018), Asfaw et al. (2019), Coromaldi (2020), Alfani et al. (2021), and Arag ́on et al. (2021).

A second data product, Tropical Applications of Meteorology using SATellite data and groundbased observations (TAMSAT), similarly makes use of rain gauge information from the GTS and Meteosat thermal infrared (TIR) data (Tarnavsky et al., 2014). Input gauge data are supplemented by collection from local sources and the relationship of rainfall to cloud cover duration (CCD) derived from TIR is optimized for spatio-temporal calibration zones. Data are produced at 0 _._ 375<sup>_◦_</sup> resolution. Eberhard-Ruiz and Moradi (2019), Mamo et al. (2019), and Morgan et al. (2019) are some of the papers which rely on TAMSAT data.

Finally, the Climate Hazards group InfraRed Precipitation with Station Data (CHIRPS) data set is a global product that builds on the same blending approach of ARC2 and TAMSAT (Funk et al., 2015). Enhancements include use of a phased approach, incorporating additional climatological products, and ingestion of gauge data from national sources. Daily data are produced at 0 _._ 05<sup>_◦_</sup> spatial resolution. A large number of papers utilize CHIRPS, including Michler et al. (2019), Shee et al. (2019), Jagnani et al. (2021), and Martey and Kuwornu (2021).

# **2.1.2 Assimilation Models**

Assimilation models 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. Reanalyses are assimilation models applied retrospectively over a given time period, to incorporate improvements in algorithms, data processing, and new data sets to support analysis of climate over the long-term. Outputs are inferred or predicted based on the system state and understanding of interactions between model variables. Outputs are differentiated at higher temporal resolution (sub-daily), as well as vertical/pressure levels, but typically lower spatial resolution than other weather data sets. We use two reanalysis data sets for both rainfall and temperature in this analysis: The European Centre for Medium-Range Weather Forecasts ERA5, gridded at 0 _._ 28<sup>_◦_</sup> , and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), on an irregular grid of 0 _._ 625<sup>_◦_</sup> _×_ 0 _._ 5<sup>_◦_</sup> (Hennermann and Berrisford, 2020; Bosilovich et al., 2016). The two sources have many inputs in common, although differ in processing routines and models. Papers which use ERA5 include Arslan et al. (2015), Asfaw and Maggio (2018), Kalkuhl and Wenz (2020), Sedova and Kalkuhl (2020), and Jagnani et al. (2021). Those which use MERRA-2 include Chen et al. (2017), Chen et al. (2018), and Letta et al. (2018).

# **2.1.3 Interpolated Gauge Data**

Last, we consider data products produced primarily from gauge data, using only spatial interpolation techniques to produce a continuous surface from observed measurements. The NOAA Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Daily Precipitation and Temperature 9