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The ensemble forecasts have 51 members, which will be assigned an extremity rank each. Using these 51 ranks the forecasts will be put in one of the 7 anomaly categories (as described in Table 1). This is done based on the arithmetic mean of the 51 ensemble member rank values (rank-mean). This rank-mean will also be a number between 1 and 100, but this time a real (not integer) number. If the anomaly is 50.5, that is exactly the normal (median) condition, i.e. no anomaly whatsoever. If the anomaly is below 50.5, then drier than normal conditions are forecast, if above 50.5, then wetter than normal. The lower/higher the anomaly value is below/above 50.5, the drier/wetter the conditions are predicted to be. The lowest/highest possible value is 1/100, if all ensemble members are 1 or /100 (the most extremely dry/wet). Then, based on this rank-mean, we define the dominant anomaly category (one of the 7 categories in Table 1) for the ensemble forecast, by placing the rank-mean into the right categories, as defined in Table 1 above. For example, all rank-mean values from 40.0 to 60.0, interpreted as 40.0<=  <60.0, will be assigned to 'Near normal', or category-4.

The ensemble forecast anomaly was not based on the most probable of the 7 anomaly categories, as that would make it prone to jumpiness. For example, in the super uncertain case of 6, 8, 7, 7, 7, 9, 7 members being in each of the 7 anomaly categories, the forecast category (the dominant one) would be the 'High' category (cat-6), as that has the most members (9). However, it is likely that nearby river pixels could easily be only slightly different with 7, 9, 7, 7, 7, 7, 7 members in each category, in which case the dominant anomaly category would be the 'Low' category (cat-2), as now that has the most (again 9) members. It is worth mentioning that very uncertain cases are especially likely to happen at longer ranges. These two forecasts are only slightly different in terms of distribution, but the ensemble forecast anomaly categories would be almost the complete opposite of each other, making the signal look possibly very jumpy geographically. With the mean-rank definition we avoid this and simply assign the 'Near normal' category (cat-4) for both these forecasts, as the mean of the ranks are certainly very close to each other and both will be quite near the median.

Figure 2. Schematic of the forecast extremity ranking of the 51 ensemble members and the 7 anomaly categories in the context of the climatological distribution.

There is a consequence of the 0-value problem over dry or very dry areas (described above), as some or all of the low anomaly signals will not be impossible to occur. Please consider these idealised examples as a help to demonstrate the situation with the 0-value problem. In these, for simplicity reason, a fixed portion of the ensemble members are being 0 and the rest having a simple average rank. This way the rank-mean and dominant anomaly category computation is simpler and easier to interpret.

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Example No-3: Lowest 70% of the climatology is 0:

Example No-4: All percentiles of the climatology (1-99) are 0:

• If all ensemble members are 0, then the ranks will spread evenly from 1 to 10 and the rank-mean will be around 5.5, so in the Extreme low category.

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