A spatial cumulative distribution functionF ∞ (say) is a random distribution function that provides a statistical summary of random field over a given region. This paper considers the empirical predictor ofF ∞ based on a finite set of observations from a region in 1 d under a uniform sampling design. A functional central limit theorem is proved for the predictor as a random element of the space D[−∞, ∞]. A striking feature of the result is that the rate of convergence of the predictor to the predictandF ∞ depends on the location of the data-sites specified by the sampling design. A precise description of the dependence is given. Furthermore, a subsampling method is proposed for integral-based functionals of random fields, which is then used to construct large sample prediction bands forF ∞ .