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).
This getting even more 'interesting' when also considering [Dask's percentile](https://docs.dask.org/en/latest/array-api.html#dask.array.percentile), which is not without problems, see e.g. [dask issue #1225](https://github.com/dask/dask/issues/1225).