Methods: These two methods empirical mode decomposition and ensemble empirical mode decomposition are interesting approach to decompose the signals into locally periodic oscillations.

Result: The Intrinsic mode functions (IMFs), will easily Identify the Embedded structures, even with those smaller amplitudes. Ensemble Empirical Mode Decomposition is better performed than the Empirical Mode Decomposition technique. The Empirical Mode Decomposition method was observe the mode mixing of two signals in Intrinsic Mode Function, but Ensemble Empirical Mode Decomposition found the distinct and clear peak of each periodic signal in each Intrinsic Mode Function.

Conclusion: In Empirical Mode Decomposition method has 11 Intrinsic Mode Functions and Ensemble Empirical Mode Decomposition has 10 Intrinsic Mode Functions, it is due to the Noise-assisted method to reduce the Intrinsic Mode Function numbers. The decomposed oscillations in Ensemble Empirical Mode Decomposition are above confidence interval and significant, it has 515 iterations than Empirical Mode Decomposition method has 740 iterations. We observe the computational time is lesser in Ensemble Empirical Mode Decomposition than Empirical Mode Decomposition method.

Key Words: Precipitation, Empirical mode decomposition (EMD), Ensemble empirical mode decomposition (EEMD), Periodic oscillations, Lomb-Scargle (LS) spectral analysis