Query rewriting is a core component of web search, yet traditional methods mainly rely on large language model (LLM) prompting, fine-tuning, or personalized rewriting based on user history. These approaches often overlook population-level intent signals in large-scale query logs, leading to misalignment with mainstream search intent. Moreover, although large models achieve high rewriting quality, their computational demands and deployment complexity limit industrial applicability. To address this, we propose Population-Guided Intent-Aware Rewriting (PGIR), which captures dominant population intent via a Semantic Clustering Unit (SCU) and generates intent-aware rewrites through a Rewriting Unit (RU), aligning queries with mainstream search goals without requiring user history.Building upon PGIR, we further introduce PGIR-DPA, a dual-phase adaptation strategy that transfers capabilities from a teacher LLM to lightweight student models, achieving high-quality rewriting while ensuring industrial scalability. Extensive offline and online experiments, including A/B testing on Baidu Search, show substantial improvements in rewrite quality and a 6.28% relative increase in user satisfaction. The framework has been fully deployed in Baidu's production search system, operating stably at scale, validating its industrial feasibility and commercial value.