Detection of Temporal Variability in U.S. Climate Using Harmonic and Wavelet Decomposition

TL;DR

This paper analyzes U.S. climate variability using harmonic and wavelet decomposition, revealing dominant periodicities and demonstrating that frequency-aware models outperform trend-only models in predicting climate fluctuations.

Contribution

It introduces a harmonic decomposition approach to characterize and improve understanding of U.S. climate variability over time.

Findings

U.S. climate exhibits dominant seasonal and ENSO-related periodicities.

Harmonic models significantly improve predictive performance over linear trends.

Climate fluctuations are characterized by quasi-stationary oscillations rather than monotonic trends.

Abstract

This study investigates temporal variability in U.S. climate using harmonic decomposition techniques, specifically Fourier and wavelet transforms. Monthly temperature, precipitation, and drought index data from the National Oceanic and Atmospheric Administration (NOAA) U.S. Climate Divisional Dataset (nClimDiv, 1895--2024) were analyzed to detect periodic structures and their evolution over time. By comparing harmonic-based models with linear regression trends, this research evaluates the explanatory power of cyclic components in reproducing and predicting observed variability. Results show that U.S. climate records exhibit dominant periodicities near one year (seasonal) and 2--7 years (associated with the El Nino--Southern Oscillation, ENSO), and that incorporating harmonic terms significantly improves model performance across most states and variables. The findings indicate that U.S. climate fluctuations are characterized by quasi-stationary oscillations rather than purely monotonic trends. Overall, the main implication is that frequency-aware models provide measurably better predictive skill than trend-only approaches and should be incorporated into seasonal outlooks, drought monitoring, and resource planning.