## 1 Introduction  
Precipitation is at the heart of the global water and energy cycle that is strongly impacted by global warming [1]. This has prompted the scientific and operational community to develop new products to support climate monitoring and a better understanding of processes [2]. At the one degree one day scale, the emergence of a suite of new and renewed precipitation estimates from in situ, reanalysis and satellite [3] has been shown to provide observational constraints for detection and attribution analysis [4], climate model validation [5, 6] and process analysis [7]. In particular, with the advent of the long time series of satellite precipitation-relevant measurements, multi-platforms satellite precipitation estimates have been shown to reveal overall good performances [8, 9].  
While at a high resolution, multi-platform precipitation products' uncertainty has actively been explored [10, 11, 12, 13, 14] at the daily accumulated scale of \(1^{\circ}\times 1^{\circ}\), less is known in terms of structural errors of the products. Numerous atmospheric processes contribute for modulating the precipitation (detection and intensity) distribution in space and time. Assessing this using satellite, on an \(1^{\circ}\)/daily scale, would support research on global precipitation (e.g., on the investigation of global extremes) and facilitate better intercomparison and validation exercises with a common gridded database. A few validation studies have nevertheless been conducted in the tropics revealing some regionally varying uncertainties that are difficult to interpret in terms of algorithms' limitations [15]. [PERSON] et al. [16] conducted a thorough error propagation analysis of the TAPEER product at a \(1^{\circ}\times 1^{\circ}\) daily scale. Their study revealed the importance of the sampling uncertainty in the error budget, compared to the Level-2 (L2) uncertainty and satellite calibration sources. It also showed the non-linearity of the L2 to Level-4 (L4) bias propagation, emphasizing the specific nature of the \(1^{\circ}\times 1^{\circ}\) accumulated daily precipitation uncertainty. Uncertainty depends on the assumed error model [17] and also relates to the sources of intrinsic errors that create it in the first place. In the case of multi-platforms satellite products, for instance, the changes in the microwave constellation configuration can impact performances and the structural errors of the final product [18; 19]. For daily gridded products, sampling errors due to a finite number of observations can significantly alter the comparison to ground-based networks [20].  
As an attempt to improve the existing framework for better understanding the uncertainty of the daily \(1^{\circ}\times 1^{\circ}\) scale, the ability of a simple decomposition of the uncertainty, under an additive error model assumption and as a sum of two Gaussian distributions was assessed. The following three types of uncertainties were considered here: (i) constellation changes-induced uncertainties, (ii) sampling uncertainties and the (iii) uncertainties related to comparisons with rain-gauge network data. Each of the uncertainties are illustrated with a multi-platform satellite precipitation product. Section 2 presents the data and details the algorithm used for the Gaussian mixture calculations. Section 3 shows the results of the fitting which are systematically contrasted with those of a single Gaussian approach. Finally, our findings are summarized and discussed in Section 4.