Climate variability highly influences water availability and demand in urban areas, but medium-term predictive models of residential water demand usually do not include climate variables. This study proposes a method to predict monthly residential water demand using temperature and precipitation, by combining a novel decomposition technique and gradient boost regression. The variational mode decomposition (VMD) was used to filter the water demand time series and remove the component associated with the socioeconomic characteristics of households. VMD was also used to extract the relevant signal from precipitation and maximum temperature series which could explain water demand. The results indicate that by filtering the water demand and climate signals we can obtain accurate predictions at least four months in advance. These results suggest that the climate information can be used to explain and predict residential water demand.

气候变率极大地影响了城市地区的水资源供应和需求，但住宅用水需求的中期预测模型通常不包括气候变量。本研究提出了一种通过结合新的分解技术和梯度提升回归，使用温度和降水来预测每月居民用水需求的方法。变分模式分解（VMD）用于过滤需水时间序列并去除与家庭社会经济特征相关的成分。VMD 还用于从降水和最高温度序列中提取相关信号，这些信号可以解释需水量。结果表明，通过过滤需水量和气候信号，我们可以至少提前四个月获得准确的预测。