The seasonal temperature effect on electrical resistivity data is often overlooked in ERT monitoring surveys and modelling efforts. This oversight can lead to anomalies in the models that are orders of magnitude greater than the target anomalies being monitored, potentially resulting in unusable or misleading results. When not overlooked, temperature correction involves costly and logistically complex measurements of ground temperature alongside resistivity data collection. In this study, we propose a novel Time-Lapse inversion scheme, named ARES, to address the seasonal temperature effect without the need of subsoil measurements. The ARES correction directly incorporates temperature into the modeling, estimating subsoil temperature by solving the heat diffusion equation for each time-step and introducing the thermal diffusivity of the medium as an inversion parameter. We present synthetic modelling to test the effectiveness of the ARES correction and develop guidelines for implementing ERT monitoring with the ARES correction. Subsequently, the application of ARES scheme to a 20-month ERT monitoring project over a Municipal Solid Waste landfill is presented, where a 3D acquisition layout is employed to observe waste evolution and identify area of high biogas productivity. Our results demonstrate that without the ARES correction, temperature effects overshadowed target anomalies, hindering interpretations. However, with the ARES correction, we successfully compensated for temperature effects in the inversion models. In the real case study, this correction enabled the detection of anomalies associated with different physical phenomena, allowing for quantitative interpretations.