## 1 Introduction  
Precipitation is an important input of water in the hydrological cycle and is the main source of catchment water [1]. Accurate and reliable precipitation information is therefore necessary to ensure better water resource management and decision-making in the various areas using water, such as agriculture, industries, and cities. Besides that, precipitation datasets are also vital in meteorology, especially for weather forecasting and prediction of water-related hazards such as floods and droughts [2; 3; 4; 5; 6]. Traditionally, precipitation data within the catchment area come from direct measurement at gauges. Apart from this, precipitation is also recorded using other ground-based observations such as weather radars and disdrometers, and these techniques are often cited as the most reliable sources of precipitation information [7]. Precipitation data obtained using ground-based observations can be very accurate; however, there are some limitations associated with their cost and the insufficient density of ground stations, especially in the less accessible mountain regions of the world [8]. Satellite precipitation products (SPPs) are widely accepted as an alternative source to overcome the limitations of ground techniques [9]. Recently, satellite information has become available at high spatial (up to 0.25\({}^{\circ}\)) and temporal (near real time) resolutions and over large areas (near global). However, estimations using SPPs are subjected to bias and stochastic errors, which depend highly on the hydro-climatic characteristics of a region [10; 11]. Therefore, performance evaluation of SPPs in different regions is essential to enable users and algorithm developers to better understand and quantify such errors. Many studies have been conducted to evaluate SPPs at a regional scale [12; 13; 14]. [PERSON] _et al._[15] found the Climate Prediction Center Morphing Technique (CMORPH) performed better at depicting the spatial and temporal patterns of precipitation over China compared to the Tropical Rainfall Measuring Mission (TRMM) 3B42 products and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Network (PERSIANN). By contrast, CMORPH was the worst estimator of precipitation patterns over Indonesia [16]. [PERSON] _et al._[17], in a previous study in Malaysia, found that TRMM 3B42 RT yielded overestimations and gauge-adjusted 3B42V7 effectively reduced the overestimations, but the study is a global scale assessment. Malaysia is a typical tropical country with a mean annual precipitation greater than 2500 mm/yr and a mean monthly temperature range from 23 to 32 \({}^{\circ}\)C. The country crucially needs reliable spatial information on precipitation as it mainly relies on agriculture and is subject to frequent floods. It is, for instance, one of the main oil-palm-producing countries, with palm oil productivity highly affected by water availability [18]. This is one reason why adequate monitoring of precipitation over the country is essential. The other reason is that floods are the main natural disaster in Malaysia, with 9% of the total land area and 22% of the population directly affected by floods. One of the worst flooding recorded in the history of Malaysia occurred during the 2006-2007 rainy season; it was responsible for a total economic loss of about $500 million, caused 16 deaths, and resulted in the evacuation of nearly 200,000 people [19,20]. The years that followed these particular catastrophic events were characterized by a search for near real time and high temporal precipitation information tools that would be adapted to the country for improved flood forecasts. Studies about the evaluation of SPPs in Malaysia appear to be limited. [PERSON] _et al._[21] evaluated the daily precipitation from TRMM 3B42V6 data in Peninsular Malaysia, which covers only about 40% of the total area of Malaysia, using for validation only four precipitation gauges.