Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar

this study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. country also undergoing extreme rainfall events such as droughts cyclones. rain gauge network Madagascar limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets fill gaps in ground-based datasets. We assessed reliability 21 reanalysis products (P-datasets) through direct comparison with 24 station measurements at monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias. Based first analysis, produced merged dataset based weighted average products. KGE CC scores, WFDEI (WATCH Forcing Data methodology applied ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) MSWEP (Multi-Source Weighted Ensemble Precipitation) are most accurate for estimating national scale. Additionally, results reveal high discrepancy between bio-climatic regions. The reveals higher performance than other all situations. These demonstrate usefulness merging approach an area deficit data climatic topographic diversity.