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Precipitation is considered one of the most important forcing data in scientific investigations involving agriculture, water management, and climate variability. Knowledge of precipitation variation during rainy seasons is the key to the understanding of the precipitation variability under the effect of climate change. This study evaluated some rainy season features (e.g., onset, retreat, and rainy-season precipitation) over the Yellow River basin (YRB), China, and the influence of the El Niño-Southern Oscillation (ENSO). The multi-scale moving t-test was applied to capture the onset and retreat of rainy season. The possible linkage between ENSO-induced precipitation and monsoon and atmospheric circulation was also explored. From the analysis, four conclusions can be drawn: (1) Rainy season began the earliest (latest) and ended the latest (earliest) in the southern (northern) YRB, with rainy-season precipitation increasing from northwest to southeast. (2) Rainy-season precipitation showed strong correlation to SST in Niño regions. Precipitation can reach up to 20% above the average precipitation for decaying central Pacific warming (CPW) and down to 35% below the average precipitation for developing eastern Pacific warming (EPW) in most areas of the YRB. (3) Developing El Niño showed the strongest dry signals among developing ENSO and ENSO Modoki phases. Decaying El Niño and El Niño Modoki indicated overall increasing precipitation, with La Niña and La Niña Modoki during the corresponding period showing the opposite tendency. (4) Different performances of ENSO-induced precipitation attributed to the combined influence of the monsoon from the India Ocean and the atmospheric circulation in the Western North Pacific (WNP). Stronger anti-cyclone and monsoon are related to increasing rainy-season precipitation. These findings can improve the predictability of rainy season features and ENSO-induced precipitation. [ABSTRACT FROM AUTHOR]

Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study. [ABSTRACT FROM AUTHOR]