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We present a new seasonal forecasting model for the June-September rains in Ethiopia. It has previously been found that the total June-September rainfall over the whole country is difficult to predict using statistical methods. A detailed study of all available data shows the rainfall seasonality varies greatly from one region to another, which would explain why the total June-September rainfall over all regions is a difficult property to forecast. In addition, the correlation between rainfall and the southern oscillation index varies spatially, with a strong teleconnection present only in some regions. This study accounts for the spatial variability in rainfall by grouping the rain gauge stations into four geographical clusters based on seasonality and cross-correlation of rainfall anomalies. Linear regression equations are then developed separately for each cluster. The variables we use for the regressions are sea-surface temperature anomalies in the preceding March, April and May of the tropical western Indian Ocean, the tropical eastern Indian Ocean, and Niño3.4. Formal skill testing of the equations shows that the new forecasting scheme is more effective in central western Ethiopia than either climatology or persistence -the methods currently used by the Ethiopian National Meteorological Services Agency.

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.