Precipitation is a complex process that is extremely variable over a wide range of space-time scales. More specifically, it is strongly intermittent: the heaviest precipitation are increasingly concentrated on sparser and sparser fractions of the space-time domain. At the same time, precipitation is a key variable of urban geosciences. Multifractals have been developed to analyse and simulate across scales this multiscale intermittency, while the climate networks can detect and characterise event synchronisation. In contrast to multifractal analysis, climate networks are usually performed at a given scale, defined by the resolution of the data. In this communication, we present how to overcome this dichotomy and propose multiscale climate networks in the hope of reaching scales relevant to urban geosciences.Specifically, we study theoretically and/or numerically the scale dependance of different centrality measures of climate networks determined at different scales by coarse graining the precipitation data, as is done for multifractal analysis. Among the preliminary results, we show how to modify some of the parameters of the climate networks to force scale invariance of their structure.