Flare removal methods eliminate reflective and scattering flares within images and commonly trained on synthetic data pairs. However, they fail to achieve robustness for real-world flare-corrupted images as the adopted synthetic training data remains gaps with real-world data. In this paper, we propose a real-captured paired dataset named FlareReal600, which contains both real-captured image pairs and pure flare images. Compared with the existing flare removal dataset Flare7k++, our dataset is particularly effective for real-world scenarios as our data contains the faithful mapping between real flare-corrupted images and real flare-free images. Additionally, previous methods either lack sufficient receptive fields or achieve them with huge computational cost, which leads to flares being partly removed or hardly processing high-resolution images. Therefore, we propose a novel flare removal network named Mutual reCeption fLAre REmoval Network (McLaren), which utilizes convolutions with diverse kernel sizes and fuses them from the perspective of both spatial and channel dimensions to achieve a sufficient receptive field. Furthermore, we employ a reparameterization mechanism to avoid occupying excessive computational resources. We conduct extensive experiments to demonstrate functions of our dataset and our McLaren network. We also employed a down-sampled version of our dataset to host the MIPI 2024 flare removal challenge.