**1. Introduction**

Droughts and floods are water-related natural phenomena which have large negative impacts on society and activities related to agriculture, and local economies. Drought is one of the most important natural disasters, since it affects wide areas for long time (months to years) and, thus, has a serious impact on regional or countries economic performance, etc.

In recent decades, large-scale extreme events (i.e., droughts) have been observed in many places around the world leading to high negative impacts on economic, ecological resources, food shortages, etc. However, floods are among the most destructive natural phenomena, declaring more lives and leading to more property damage than any other natural events. The reliable and accurate drought and flood information have been more interested for a variety of authorities, such as water managers, policy makers, researchers, farmers, etc., for effective management [1].

Since precipitation is the most important factor of the aforementioned phenomena, knowing the locations, domain, and length of precipitation is essential to understand, predict, and mitigate the impact of such disasters. Irrespective of the less accessible mountainous and oceanic regions, compared to ground-based measurements, such as gauges and radars, satellite precipitation estimates (SPE) products are able to cover the precipitation system at a nearly global-scale. Generally, in situ observations are often subject to wind effects, many missing values, insufficient number of stations, or sparse gauge networks, particularly in mountainous or desert areas [2,3]. Moreover, gridded daily surface precipitation data are important for many water-related applications, such as drought and flood monitoring systems. Rapid growth in computer technology and the remote sensing area help observations processed from satellites, individually, and merge them with other data sources to provide a better understanding of the precipitation spatial visualization. The information derived from SPEs provides tremendous potential for identification, monitoring, and assessment of droughts, flood, etc., especially for regions with sparse rain gauges or limited radar coverage [4].

However, the precision of SPEs at spatiotemporal representations has a great influence on the effective predictions of natural hazard, climate impacts, etc.; therefore, accuracy analysis of the new precipitation products is often applied before it can be employed in decision-making activities [5]. The satellite/gridded data produce the area average of precipitation in contrast with point measurements obtained with rain gauges. Earlier studies have shown that diverse altitude and geographic and climate conditions have greatly impact on the accuracy and performance of satellite or other precipitation products [2,5–11]. For instance, in a study by Gottschalck et al. [12] the overestimation of 3B42-RT over the Central United States is attributed to misclassification of cold cirrus clouds as precipitating systems. In another study, Dinku et al. [13] demonstrated that topography plays a significant role in SPE due to the weakness of algorithms to detect orographically-induced precipitation. Numerous studies have shown that an appropriate interpolation method might develop a gridded dataset using the rain gauges, but the obtained dataset is dependent on both adequate underlying station observations and the use of an appropriate interpolation technique to produce high-resolution gridded point estimates prior to the creation of area-averages grid values [14]. Thaler et al. [15] used different gridded precipitation data in an agronomic application and analyzed how different products influence a crop model application. The Austrian radar network (Austrocontrol) is operated consisting of four radar stations distributed across the country. However, due to the mountainous area and terrestrial characteristics of the country, in many western regions of Austria radar data have limitation to use particularly during wintertime, when precipitation may originate from rather shallow cloud systems.

In recent years, with the rapid development of remote sensing technique, more and more quasi-global satellite precipitation products have been produced and released to the public. The objective of this paper is inter-comparison of the recently released gridded precipitation products (e.g., MSWEP-V2.2, IMERG-V05B, IMERG-V05-RT -V06A, ERA5, and SM2RAIN) against in situ observations. The question motivating this study is "To what extent have the recently released gridded precipitation products, improved as compared to a dense rain-gauge network?" The total error is broken down into estimation and detection of precipitation in order to assess the algorithm performance than can highlight the weakness and strength of those algorithms and assist the developer to improve those aspects that have greater need.

In this study, the gauge-based measurements are directly compared against the corresponding gridded data, enabling us to identify the best precipitation estimated product and how close they are to source data (stations).

It is noteworthy to mention that, previously, Sharifi et al. [3] conducted a study to evaluate the reliability of the IMERG-V04 final-run (FR) and real-time (RT) products against the Central Institute for Meteorology and Geodynamics (ZAMG) stations over Northeast Austria. However, an evaluation of the performances of ERA5, MSWEP-V2.2, IMERG-V5B, IMERG-V6, IMERG-V5-RT, and SM2RAIN products has not been conducted over Austria. Therefore, the aforementioned products and a highly dense in situ precipitation network provided by the Federal Ministry for Sustainability and Tourism (BMNT)-Austria (882 stations) are selected in this study. Lastly, this study will be useful since it will provide the reference for precipitation monitoring and regional climate prediction across Austria.

### **2. Data and Study Area**

The data used in this study are described below and its summaries and characteristics have been shown in Table 1.


**Table 1.** Characteristics of the precipitation products.
