Hydrological analyses carried out based on precipitation in the Brazilian Legal Amazon (BLA) are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, data series with short periods and many gaps, especially at the daily scale, are a limitation in this region. In order to improve precipitation analysis, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in the BLA. For this purpose, 22 rain gauge stations were adopted and organized, taking into account the complete series and the seasonal periods (rainy and dry).The results obtained showed a good performance of the model, with ranges of MSE (0.0022–0.2665), MAPE (0.0083–1.5343) and RMSE (0.0017–0.0214), which characterize an acceptable estimate for the estimation daily precipitation, especially in those with a wetter climate and more frequent precipitation during the year, as is the case in those located in the Amazon Biome. However, in regions that suffer from droughts, such as the Amazon-Cerrado ecotone areas, the results were less satisfactory due to the greater recurrence of zeros in the historical series. The seasonal division into dry and rainy periods did not provide better estimates for the model, except for some rain gauge stations located at latitudes close to the equator. However, this study could support future research on the estimation of daily precipitation in the region.

基于巴西合法亚马逊 (BLA) 降水进行的水文分析至关重要，因为它们在气候调节以及区域和全球大气环流中具有重要意义。然而，周期短、间隙大的数据系列（尤其是日尺度数据）是该区域的限制。为了改进降水分析，使用基于人工神经网络（ANN）的非参数随机模型来估计BLA中的日降水量。为此，考虑到完整的系列和季节（雨季和旱季），采用和组织了 22 个雨量站。获得的结果表明该模型具有良好的性能，MSE 范围为（0.0022–0.2665）， MAPE (0.0083–1.5343) 和 RMSE (0.0017–0.0214)，这两个参数代表了每日降水量的可接受估计值，特别是在那些气候较湿润且一年中降水更频繁的地区，如位于亚马逊地区的情况生物群落。然而，在遭受干旱的地区，例如亚马逊-塞拉多交错带地区，由于历史序列中出现更多的零，结果不太令人满意。除了一些位于接近赤道的纬度的雨量站外，季节划分为干旱期和雨期并没有为模型提供更好的估计。然而，这项研究可以支持未来对该地区日降水量估算的研究。