## 2 Overview of Contributions  
[PERSON] et al. [1] aimed to evaluate the accuracy of ERA5 precipitation products for two ecoregions of the Canadian Prairies and compare them with monthly means measured from 1981 to 2019 at ten weather stations. Their study assessed intraseasonal variability in precipitation and identified dry and wet periods based on the annual Standardized Precipitation Index (SPI). A significant relationship between in situ data and ERA5 data was observed in nine of the ten weather stations analyzed. The analysis of wet and dry periods, based on the SPI derived from ERA5, and the comparison of these data with events associated with the El Nino-Southern Oscillation (ENSO) showed that from the ERA5 data and the derivation of the SPI, it was possible to identify temporal anomalies with consistent patterns that can be associated with historical events. [PERSON] et al. [2] aimed to quantify the competence of six gridded precipitation products (GPPs), at multiple time scales (daily, monthly, and yearly), using over 1.7 million in situ observations throughout Iran over the past two decades (2000-2020). Both continuous and categorical metrics were implemented to assess precipitation intensity and occurrence based on the point-to-pixel comparison approach. Although not all metrics support the superior performance of any specific GPP, the findings suggested that the Global Satellite Mapping of Precipitation (GSMaP) provides better estimates of daily precipitation. Based on the obtained continuous metrics, all the GPPs showed better performance in the dry months, while this was not the case for the categorical metrics. [PERSON] and [PERSON] [3] sought to provide insight into interpolated gauge product accuracy at low gauge densities using a Monte Carlo interpolation scheme at locations across continental U.S. and Brazil. They hypothesized that errors in interpolated precipitation estimates from the GPM (Global Precipitation Measurement) mission increased drastically at low rain gauge densities and at high distances from the nearest gauge. The results showed that the Integrated Multi-satellite Retrievals for GPM (IMERG) product had comparable precipitation detection performance to interpolated gauge data at very low gauge densities (i.e., less than 2 gauges/10,000 km\({}^{2}\)), and that IMERG often outperformed interpolated data when the distance to the nearest gauge during interpolation was greater than 80-100 km. In a comprehensive analysis of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products, [PERSON] et al. [4] evaluated the capability of these precipitation estimates to represent precipitation characteristics over Luzon, which is the largest island in the Philippines. The analyses focused on monthly and daily timescales from 2003 to 2015, and adopted surface observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) platform as their evaluation base. Among several satellite precipitation products, PERSIANN-CDR was observed to more effectively qualitatively and quantitively estimate spatiotemporal variations in precipitation over Luzon for the majority of the examined features, with the exception of extreme precipitation events. [PERSON] et al. [5] outline the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission and provided a description of the retrieval scheme for the Millimeter-wave Sounder (TMS). They present the initial results of precipitation estimates using the Precipitation Retrieval and Profiling Scheme (PRPS). The TROPICS mission consists of a Pathfinder CubeSat and a constellation of six CubeSats, providing a low-cost solution to the frequent sampling of precipitation systems across the Tropics. These results showcase the potential of the TMS instrument for the retrieval of precipitation data.