To develop a baseline “no climate change” scenario, we use historical daily climate data for 1997-2006 (i.e., mean, minimum and maximum surface temperatures and precipitation) retrieved from the NASA POWER database (Stackhouse, 2010).<sup>2</sup> A random 50-year baseline climate sequence was generated using bootstrapping techniques based on the historical annual data. In other words, one year of climate is drawn from the historical data fifty times. This technique preserves intra-annual correlations (and higher moments) but not inter-annual correlations. For the purposes of the crop modeling, the distinct wet and dry seasons in Tanzania limit the impact of inter-annual variation in soil moisture at the start of the growing seasons rendering this approach an acceptable compromise in the face of existing data limitations. Our baseline scenario therefore assumes that future weather patterns will retain the characteristics of recent historical climate variability. The principal purpose of the baseline scenario is to provide a counterfactual for the climate change scenarios.

For the purposes of projecting future climate in a given region (Tanzania), it is useful to note that the GCMs are calibrated to reasonably reproduce historical climate on a global basis and predict future climate under alternative levels of global greenhouse gases. The historical predictions of a given GCM for temperature and precipitation for a specific region of the globe, such as Tanzania, will not perfectly match the historical data. In other words, output from a given GCM for historical periods may, for example, consistently under-predict temperature and over-predict precipitation. This is also true with respect to higher order moments and time series properties of the climate series.

In sum, while the GCMs provide information with respect to long run trends in average temperatures and potential trends in average precipitation, it is less clear that the outputs of the GCMs provide useful information with respect to daily variation in climate (Schlosser, 2011). As a result, it is inappropriate to directly take the raw output from GCMs and use these in, for example, crop models for Tanzania estimated on the basis of historical data. Some manipulation of the GCM output is required. Here, we combine the long run temperature and precipitation changes predicted by each GCM with historical data on daily climate variation. Specifically, we calculate a ten-year moving average of the percentage monthly changes in temperature and precipitation between the selected climate change scenario as predicted by the GCM and a baseline no climate change scenario from the same GCM. So, the percentage change in temperature for January 2030 is the average percentage change in temperature recorded by the GCM for the periods January 2026 to January 2035. We then overlay these changes on the baseline historical scenario. For example, when the moving average from a GCM predicts a five percent increase in January temperature and a three percent decrease in precipitation for a given year, then the January baseline temperatures and precipitations (drawn from the historical record) are augmented by those percentages.

> 2 A longer historical data series would be desirable.

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