We construct six monthly abnormal weather indicators by employing physical weather data of temperature, humidity, wind speed, precipitation, sunshine hours and sky coverage in China. We compute the abnormal weather betas to proxy for abnormal weather risks and investigate the pricing effects of these risks in the Chinese stock market. We find a low abnormal weather beta premium in the cross-section, i.e., stocks with high betas to changes in the abnormal weather index significantly underperform those with low betas by 0.10% to 0.37% per month on a risk-adjusted basis. In particular, the low abnormal weather beta premiums of temperature, sunshine hours, and sky coverage are significant over the subsequent six months. The further analyses suggest that the abnormal weather betas exhibit persistent long-term predictive power and have significant explanatory power for size, value, and long-term reversal anomalies. We further explore several potential explanations for the low abnormal weather beta premiums from various aspects, including firm characteristics, industry heterogeneity, firm performance, investor behavior and institutional holdings. The results suggest that neither firm-level characteristics nor industry heterogeneity can adequately explain the low abnormal weather beta premiums. The anomalies related to abnormal weather sensitivity are likely driven by the interaction of investor behavioral bias and arbitrage barriers, rather than compensation for fundamental risk.