## 2 Materials and Methods  
### Data Collection  
To assess the readiness of Africa to adapt to climate change, this study uses both climate and adaptive capacity proxy-based data. The data were collected for each country in Table 1 and compiled to provide regional averages for each of the five key regions of Africa, which include (North Africa, West Africa, Middle/Central Africa, East Africa, and Southern Africa. The climate data constitutes monthly average time series or historical precipitation (mm) and temperature (\({}^{\circ}\)C) data for 1991-2016. The precipitation and temperature data were collected from the Climate Change Knowledge Portal of the World Bank: [[https://climateknowledgeportal.worldbank.org](https://climateknowledgeportal.worldbank.org)]([https://climateknowledgeportal.worldbank.org](https://climateknowledgeportal.worldbank.org)) (accessed on 12 March 2021) [22]. To download this data, this work keyed into the download climate data option, the historical data route, and then specified the climate data to be downloaded (either precipitation or temperature), specified the country and, the time frame (1991-2016). Alternatively, the climate data for any country/region can be easily downloaded if the longitude and latitude of the location are known and computed into the platform. For details on all the data used in this study, please see supplementary materials. The World Bank has made this data accessible to the public through its collaboration with three major partners, which are: The National Centre for Atmospheric Research, which assist in processing the data, The International Research Institute of the University of Colombia, which provides analytical support and development of mapRooms as well as the European Space Agency's Earth Observation for access to earth observation datasets. Once the climate data were collected, the next step was to gather data required for computing the adaptive capacity for the different countries of Africa. Since adaptive capacity is difficult to measure, this work has based its evaluation of adaptive capacity on essential proxies of adaptive capacity. Adaptive capacity is the ability of a system, farmers, or people, in general, to cope with climate stressors or shocks such as declining precipitation and rising temperatures and their resulting impacts [13]. A region with a higher adaptive capacity can often be termed a region with higher readiness for adaptation because there is an inbuilt capacity to adjust and cope with climate stressors [23]. Therefore, for this work, we selected two proxies of adaptive capacity, which are literacy and poverty rates. We believe that these proxies are pertinent. Indeed, literacy rates used here represent the ability of the people to read, write, and therefore understand climate-related information or communications. It is evident that when people are literate, they are more likely to understand climate-related information and use it to adapt or cope when events of climate stress are reported. On the other hand, less literate people may lack the understanding and ability to apply climate-related information and communications in their adaptation decisions [24]. On the other hand, poverty rates are used here to represent the ability of the people concerned to adjust to climate stressors by obtaining alternative means of sustenance. Here, the poverty rate is based on household income. In other words, when poverty rates are high, farmers are less likely to cope and adapt by purchasing high-yielding varieties, investing in irrigation, or other farm inputs that can increase resilience [25]. Across Africa, these two proxies are very important representations of adaptive capacity because many other potential proxies are affected by these two proxies. This implies that changes in poverty and literacy rates are likely to impact the ability of different systems and people to cope and adjust to climate stressors [13].