An enhanced copula-based daily rainfall disaggregation model is presented. The rainfall data are grouped into maximum daily temperature quantiles instead of the usual monthly grouping in order to account for seasonal temperature shifts. Empirical and statistical evidence provided supports the conditioning of the model parameters on temperature. Linkage of the major model parameters to maximum daily temperature allows easy evaluation of temperature changes on fine timescale rainfall statistics. Rainfall data sets for three Australian capital cities located in different climatic regions were used to test the applicability of the presented model. It is observed that statistics such as the total wet periods’ duration, storm event duration, and the autocorrelations decrease with increase in temperature, while the maximum wet period depth, variance, skewness, and the intensity-duration-frequency (IDF) show an increasing trend with temperature. Considering the 24h wet day rainfall data sets, a 1C rise in temperature could cause rates of change, depending on the climate (lowest rates for cool temperate zones and highest for the hot tropics), of 2%–14% in the total wet periods’ duration, 5%–16% in variance, 10%–30% in skewness, 5%–9% in maximum period depth (including IDF), and 4%–25% in autocorrelations. Simulating single events of varying depths, and using temperature values spanning the spectrum of the recorded data for the capital cities, the average rates were 2%–12% for duration (and total wet periods’ duration), 10%–26% for variance, 20%–32% for skewness and 1%–20% for the autocorrelations, all of which match the 24h wet day results very well.