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The IRIS "Fast percentile issue" [#3294](https://github.com/SciTools/iris/issues/3294): This touches on our needs because the ETCCDI is rather picky about percentiles. According to them (as per implementation in reference code), the method to calculate the percentile should be [Hyndman & Fan method #8](https://www.researchgate.net/profile/Rob_Hyndman/publication/222105754_Sample_Quantiles_in_Statistical_Packages/links/02e7e530c316d129d7000000.pdf) /1/. This is also the preferred method by [NIST](https://www.itl.nist.gov/div898//software/dataplot/refman2/auxillar/percenti.htm). This method is available in the [R package](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html) and in [scipy.stats.mstats.mquantile](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.mquantiles.html). The method is however not available in [numpy.percentile](https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html), and there seems to be some [confusion regarding methods and their implementation](https://github.com/numpy/numpy/issues/10736).
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[1] Hyndman, R.J.; Fan, Y., 1996. American Statistician, 50 (4): 361–365. doi:10.2307/2684934. JSTOR 2684934. |
