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Geophysical fields are extremely variable over a wide range of space–time scales. More specifically, they are intermittent in the sense that the strongest fluctuations are increasingly concentrated in sparser and sparser fractions of the space–time domain. Multifractals have been developed to analyze and simulate intermittency across scales, while climate networks can detect and characterize extreme-event synchronization. In contrast to multifractal analysis, climate networks are usually generated at a given observation scale despite displaying complex structures over larger scales and being likely to exhibit similar complexity at smaller scales.

In this letter, we present how to overcome this dichotomy of approaches by analyzing in detail the effects of increasing the observation scale for climate networks as allowed by empirical data; i.e., how do they upscale? This must be understood as a preliminary step to be able to downscale them, including for practical applications such as urban geosciences that require the analysis and simulation of intermittent fields at a very high resolution. This is one of the reasons why we are using precipitation to illustrate our multifractal climate network approach.