**1. Introduction**

Rainfall is a key component in the hydrological cycle and the primary source of freshwater in many regions. However, climatic extremes, such as floods and landslides that are caused by heavy rainfall events, pose a great threat to communities causing loss of life and damage to properties [1,2]. Flood monitoring and water management applications require a high accuracy representation of rainfall in extreme conditions [3]. This is of particular importance in tropical monsoonal climates, such as the Mekong basin region, where convective storm events are localised in space and time.

Traditionally, rain gauges and ground-based weather radar networks have provided the most reliable precipitation data at the catchment scale [4]. However, in many areas around the world, these data are either scarce or not available. An irregular distribution of these ground-based observation stations makes it difficult to discern spatiotemporal features of convective storms and their associated rainfall fields. Therefore, satellite-based remote sensing measurements have become an important source of rainfall data e.g., [5–7]. These satellite-based measurements enable monitoring of storm events at a quasi-global scale in near real-time. In comparison to other remote systems, such as ground-based weather radar, satellite sensor observations provide a wider coverage, being able to acquire data on storm systems in regions where topographic variations limit or obstruct weather radars [8].

Satellite-based precipitation estimates are derived by combining visible to long-wave infrared (VIS/IR) sensors from the Geosynchronous Earth Orbit (GEO) satellite with Passive Microwave (PMW) sensors from the Low Earth Orbit (LEO) satellites. VIS/IR sensors are relevant to measure albedo and cloud top temperature with a high temporal and spatial resolution [9,10]. On the other hand, PMW sensors can penetrate clouds for measuring thermal emissions, which are attenuated by raindrops with a 3-h interval [11,12]. Currently available and commonly used Satellite-based Precipitation Products (SPP) include the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [13], the NOAA Climate Prediction Center MORPHing technique (CMORPH [14]), the Multi-satellite Precipitation Analysis from the Tropical Rainfall Measurement Mission (TMPA) [15,16], and the Integrated Multi-satellitE Retrievals from the Global Precipitation Measurement (GPM-IMERG) [17]. Sun et al. [18] details a comprehensive review of the main global available satellite-based precipitation datasets.

Despite advances in the field of remote sensing, SPP information is subject to several systematic and random errors that require correction e.g., [19–21]. A wide range of bias correction methodologies has been developed to improve the performance of SPP, leveraging ground-based observations. Several examples include linear scaling, local intensity scaling, the power and distribution transformation methods, and Gamma Quantile mapping e.g., [8,22,23]. These methods all adjust SPP as function of rainfall intensity values, ignoring important systematic errors, such as those that are caused by displacement and timing.

In the field of weather forecasting, displacement error in storm prediction has been taken using spatial verification methods into account [24–26]. These "nontraditional" methods do not rely on point-to-point matches between the observed and estimated fields for avoiding double penalties (e.g., rainfall estimated but not observed and vice versa) that are commonly found in traditional approaches. Methods can be broadly grouped into neighbour or fuzzy [27,28], scale separation [29–31], object-based e.g., [24–26], and field transformation [32,33]. The first two categories can be described as spatial filtering methods, in which the verification statistics are evaluated at coarser resolutions to provide information about the scale of the performance. Object-based and field transformation are considered as displacement verification methods when estimated rainfall fields, defined as an object, are spatially manipulated (displacement, rotation, scaling, etc.) to try to fit the observed value.

Several studies have used spatial verification methods to analyse and correct systematic error of SPP based on the characteristics of matched storm objects, such as location, rotation, intensity, and shape [34–36]. For instance, Demaria et al. [37] used the object-based method, Contiguous Rainfall Analysis (CRA, Ebert and McBride [24]), to correct the location error of CMORPH, PERSIANN, and the TMPA datasets over the Plata basin. Recently, Le Coz et al. [38] used the field transformation method, called Feature Calibration and Alignment technique (FCA), to correct the error due to location in the GPM-IMERG late version over Sub-Saharan Africa. These methods have been useful for correcting the displacement errors when the grid resolution is high and the storm event is small, while preserving the higher spatial variability of SPP storm. However, these methodologies are constrained by the two-dimensional analysis of the storm event.

The spatiotemporal analysis can provide a much deeper analysis on aspects of the entire life-cycle of the storm event, including time span, speed, evolution, among others. In the literature, error analysis using spatiotemporal approaches has been useful to evaluate the performance of several spatial rainfall products. For example, Ref. [39,40] used the Object-Based Diagnostic Evaluation time-domain (MODE-TD) that was proposed by Bullock [41] to evaluate the convection-allowing forecast from the Weather Forecast Model over the United States. Recently, Laverde-Barajas et al. [42] used the Spatiotemporal Contiguous Rainfall Analysis (ST-CORA; Laverde-Barajas et al. [43]) in the Southeast region of Brazil for analysing the error composition of the CMORPH SPP and evaluated the individual hydrological response of two systematic error sources: location and magnitude. This study demonstrated the importance of spatial and temporal storm characteristics to analyse the main systematic error sources in SPP.

Spatiotemporal storm analysis incorporated into bias correction methods is key to reduce several sources of systematic error in SPP. In this study, we present a spatiotemporal object-based bias correction method to reduce several systematic errors in storm events estimated by satellite. The method, called Spatiotemporal Contiguous Object-based Rainfall Analysis for Bias Correction (ST-CORAbico), uses the main storm characteristics of satellite and observed events detected by the ST-CORA method to remove errors due to displacement in space and time and volume. This method is evaluated over the lower Mekong Basin in Thailand to correct several storm event types in the Integrated Multi-satellitE Retrievals for GPM (GPM-IMERG) early version during the monsoon season from 2014 to 2017. The performance of ST-CORAbico is compared against two widely used probabilistic methods—Distribution Transformation and Gamma Quantile Mapping. This manuscript is organised, as follows: Section 2 describes the study and the rainfall data-sets; Section 3 details the methodology of ST-CORAbico; Sections 4 and 5 contain the results and associated discussion; and finally, Section 6 presents the conclusions and future work.
