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The computation of wildfire potential usually requires continuous daily data. Thus, only six stations in the Ecuadorian Andean region were suitable for this study. The selected stations are representative of the ecosystems that are prone to wildfire. The available records in these stations spanned the period 1997-2012. Yet, the period of analysis is short. Thus, Reanalysis data was bias-corrected to extend the availability of records.   The bias-correction was necessary for several reasons. First, 1000 hPa reanalysis data does not represent surface level values. There Second, there  was a 3-hour difference between the time of the two dataset's values. Reanalysis Third, reanalysis  data in the highlands may differ in quality to sea-level outputs. Thus, we plotted probability density functions (PDFs) of the reanalysis and weather station datasets. This allowed to determine the differences between their data distributions. Applying linear scaling techniques was a suitable approach to bias-correct the required variables. The aim of the study was to estimate wildfire potential for the entire country. Ecuadorian Andean Region.  Thus, a calculation of daily averages for each wildfire weather variable yielded regional values. With the reanalysis dataset the procedure is straightforward. These data is spatially and temporally continuous. However, the weather stations data had some missing days. Therefore, calculating averages over daily anomalies made the result less sensitive to missing data. \subsubsection{Forest Fire Danger Index (FFDI) calculation}  Ecuador currently does not have any scientifically designed wildfire potential index. Therefore, an alternative approach was to use an international index suitable for this region. The McArthur Forest Fire Danger Index (FFDI) is an Australian wildfire potential index \cite{Noble1980}. This index uses daily values of maximum temperature, minimum  relative humidity and maximum  wind speed. The index also incorporates a drought factor. This factor represents changes in soil moisture. the availability of fuels.  This is an indirect measure of measured indirectly using soil moisture indexes such us  the wildfire fuel availability. Keetch–Byram drought index \cite{KeetchJJ}.  This empirical index is valid for the Southeastern forest ecosystem of Australia. In this wildfire-prone ecosystem, the eucalyptus is the dominant vegetation. On the other side of the world, Latin-America —including Ecuador— experienced a massive introduction of this specie \cite{Anchaluisa2013}.Thus, is reasonable to expect that this index may represent the Ecuadorian wildfire potential. The application of this index with weather station and reanalysis data yielded daily values. With weather station data the calculation comprised the period 1997-2012. The bias-corrected reanalysis data allowed to extend the method for the period 1963-2012. For each year, a sum of the daily FFDI values provided a seasonal metric of wildfire potential. This cumulative FFDI corresponds to the wildfire season July-August-September (JAS). Finally, we calculated the 85th percentile on the seasonal FFDI time series. This determined which were extreme wildfire seasons during the period 1963-2012.