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Dynamical systems in nature exhibit self-similar fractal space-timefluctuations on all scales indicating long-range correlations and therefore thestatistical normal distribution with implicit assumption of independence, fixedmean and standard deviation cannot be used for description and quantificationof fractal data sets. The author has developed a general systems theory whichpredicts the following (i) The fractal fluctuations signify an underlying eddycontinuum, the larger eddies being the integrated mean of enclosedsmaller-scale fluctuations. (ii) The probability distribution of eddyamplitudes and the variance (square of eddy amplitude) spectrum of fractalfluctuations follow the universal Boltzmann inverse power law expressed as afunction of the golden mean. The predicted distribution is very close tostatistical normal distribution for moderate events within two standarddeviations from the mean but exhibits a fat long tail that are associated withhazardous extreme events. Continuous periodogram power spectral analyses ofavailable GHCN annual total rainfall time series for the period 1900 to 2008for Indian and USA stations show that the power spectra and the correspondingprobability distributions follow model predicted universal inverse power lawform signifying an eddy continuum structure underlying the observedinter-annual variability of rainfall. Global warming related atmospheric energyinput will result in intensification of fluctuations of all scales and can beseen immediately in high frequency (short-term) fluctuations such asdevastating floods/droughts resulting from excess/deficit annual,quasi-biennial and other shorter period (years) rainfall cycles.

Universal Inverse Power law distribution for Fractal Fluctuations in  Dynamical Systems: Applications for Predictability of Inter - annual  Variability of Indian and USA Region Rainfall